Abstract
Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and depth ambiguity. As a result, radiance fields, which have shown great promise in capturing high-frequency appearance and geometry details in dense setups, perform poorly when naïvely supervising them on sparse camera views, as the field simply overfits to the sparse-view inputs. To address this, we propose MetaCap, a method for efficient and high-quality geometry recovery and novel view synthesis given very sparse or even a single view of the human. Our key idea is to meta-learn the radiance field weights solely from potentially sparse multi-view videos, which can serve as a prior when fine-tuning them on sparse imagery depicting the human. This prior provides a good network weight initialization, thereby effectively addressing ambiguities in sparse-view capture. Due to the articulated structure of the human body and motion-induced surface deformations, learning such a prior is non-trivial. Therefore, we propose to meta-learn the field weights in a pose-canonicalized space, which reduces the spatial feature range and makes feature learning more effective. Consequently, one can fine-tune our field parameters to quickly generalize to unseen poses, novel illumination conditions as well as novel and sparse (even monocular) camera views. For evaluating our method under different scenarios, we collect a new dataset, WildDynaCap, which contains subjects captured in, both, a dense camera dome and in-the-wild sparse camera rigs, and demonstrate superior results compared to recent state-of-the-art methods on, both, public and WildDynaCap dataset.
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References
Antoniou, A., Edwards, H., Storkey, A.: How to train your MAML. arXiv preprint arXiv:1810.09502 (2018)
Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In: ICCV (2021)
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-NeRF 360: unbounded anti-aliased neural radiance fields. In: CVPR (2022)
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-NeRF: anti-aliased grid-based neural radiance fields. In: ICCV (2023)
Bühler, M.C., et al.: Preface: a data-driven volumetric prior for few-shot ultra high-resolution face synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3402–3413 (2023)
Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: TensoRF: tensorial radiance fields. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 333–350. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_20
Chen, A., et al.: MVSNeRF: fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14124–14133 (2021)
Collet, A., et al.: High-quality streamable free-viewpoint video. ACM Trans. Graph. (ToG) 34(4), 1–13 (2015)
Davydov, A., Remizova, A., Constantin, V., Honari, S., Salzmann, M., Fua, P.: Adversarial parametric pose prior. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
De Luigi, L., Li, R., Guillard, B., Salzmann, M., Fua, P.: DrapeNet: garment generation and self-supervised draping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)
Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: CVPR (2022)
Gropp, A., Yariv, L., Haim, N., Atzmon, M., Lipman, Y.: Implicit geometric regularization for learning shapes. In: Proceedings of Machine Learning and Systems 2020, pp. 3569–3579 (2020)
Gu, J., et al.: NerfDiff: single-image view synthesis with nerf-guided distillation from 3D-aware diffusion. In: International Conference on Machine Learning (2023)
Wang, G., Chen, Z., Loy, C.C., Liu, Z.: SparseNeRF: distilling depth ranking for few-shot novel view synthesis. Technical report (2023)
Guo, K., et al.: The relightables: volumetric performance capture of humans with realistic relighting. ACM Trans. Graph. (ToG) 38(6), 1–19 (2019)
Habermann, M., Liu, L., Xu, W., Pons-Moll, G., Zollhoefer, M., Theobalt, C.: HDHumans: a hybrid approach for high-fidelity digital humans. Proc. ACM Comput. Graph. Interact. Tech. 6(3), 1–23 (2023)
Habermann, M., Liu, L., Xu, W., Zollhoefer, M., Pons-Moll, G., Theobalt, C.: Real-time deep dynamic characters. ACM Trans. Graph. 40(4), 1–16 (2021)
Habermann, M., Xu, W., Zollhoefer, M., Pons-Moll, G., Theobalt, C.: LiveCap: real-time human performance capture from monocular video. ACM Trans. Graph. (TOG) 38(2), 1–17 (2019)
Habermann, M., Xu, W., Zollhoefer, M., Pons-Moll, G., Theobalt, C.: DeepCap: monocular human performance capture using weak supervision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)
Hadwiger, M., Al-Awami, A.K., Beyer, J., Agus, M., Pfister, H.: SparseLeap: efficient empty space skipping for large-scale volume rendering. IEEE Trans. Vis. Comput. Graph. 24(1), 974–983 (2017)
Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149–5169 (2021)
Huang, Y., et al.: TeCH: text-guided reconstruction of lifelike clothed humans. In: International Conference on 3D Vision (3DV) (2024)
Huber, P.J.: Robust estimation of a location parameter. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics: Methodology and Distribution. SSS, pp. 492–518. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_35
Jiang, W., Yi, K.M., Samei, G., Tuzel, O., Ranjan, A.: NeuMan: neural human radiance field from a single video. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 402–418. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_24
Jiang, Y., Habermann, M., Golyanik, V., Theobalt, C.: HiFECap: monocular high-fidelity and expressive capture of human performances. In: BMVC (2022)
Johnson, E.C., Habermann, M., Shimada, S., Golyanik, V., Theobalt, C.: Unbiased 4D: monocular 4D reconstruction with a neural deformation model. In: Computer Vision and Pattern Recognition Workshops (CVPRW) (2023)
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42(4) (2023). https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)
Kwon, Y., Kim, D., Ceylan, D., Fuchs, H.: Neural human performer: learning generalizable radiance fields for human performance rendering. In: Advances in Neural Information Processing Systems, vol. 34, pp. 24741–24752 (2021)
Kwon, Y., Liu, L., Fuchs, H., Habermann, M., Theobalt, C.: DELIFFAS: deformable light fields for fast avatar synthesis. In: Advances in Neural Information Processing Systems (2023)
Li, K., Malik, J.: Learning to optimize. arXiv preprint arXiv:1606.01885 (2016)
Li, R., et al.: TAVA: template-free animatable volumetric actors. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 419–436. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_25
Li, Y., Habermann, M., Thomaszewski, B., Coros, S., Beeler, T., Theobalt, C.: Deep physics-aware inference of cloth deformation for monocular human performance capture. In: 2021 International Conference on 3D Vision (3DV), Los Alamitos, CA, USA, pp. 373–384. IEEE Computer Society (2021). https://doi.org/10.1109/3DV53792.2021.00047. https://doi.ieeecomputersociety.org/10.1109/3DV53792.2021.00047
Li, Z., Zheng, Z., Zhang, H., Ji, C., Liu, Y.: AvatarCap: animatable avatar conditioned monocular human volumetric capture. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13661, pp. 322–341. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19769-7_19
Liu, L., Habermann, M., Rudnev, V., Sarkar, K., Gu, J., Theobalt, C.: Neural actor: neural free-view synthesis of human actors with pose control. ACM Trans. Graph. 40(6), 1–16 (2021). (ACM SIGGRAPH Asia)
Long, X., Lin, C., Wang, P., Komura, T., Wang, W.: SparseNeuS: fast generalizable neural surface reconstruction from sparse views. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 210–227. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_13
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. 34(6), 248:1–248:16 (2015). (Proc. SIGGRAPH Asia)
Luvizon, D., Golyanik, V., Kortylewski, A., Habermann, M., Theobalt, C.: Relightable neural actor with intrinsic decomposition and pose control. In: European Conference on Computer Vision (ECCV) (2024)
Ma, Q., et al.: Learning to dress 3D people in generative clothing. In: Computer Vision and Pattern Recognition (CVPR) (2020)
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: learning 3D reconstruction in function space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4460–4470 (2019)
Mihajlovic, M., Bansal, A., Zollhoefer, M., Tang, S., Saito, S.: KeypointNeRF: generalizing image-based volumetric avatars using relative spatial encoding of keypoints. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13675, pp. 179–197. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19784-0_11
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24
Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. (ToG) 41(4), 1–15 (2022)
Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015)
Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)
Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: RegNeRF: regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5480–5490 (2022)
Palafox, P., Sarafianos, N., Tung, T., Dai, A.: SPAMs: structured implicit parametric models. In: CVPR (2022)
Pan, X., Yang, Z., Ma, J., Zhou, C., Yang, Y.: TransHuman: a transformer-based human representation for generalizable neural human rendering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3544–3555 (2023)
Pang, H., Zhu, H., Kortylewski, A., Theobalt, C., Habermann, M.: ASH: animatable Gaussian splats for efficient and photoreal human rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1165–1175 (2024)
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: DeepSDF: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 165–174 (2019)
Pavlakos, G., et al.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10975–10985 (2019)
Peng, S., et al.: Animatable neural radiance fields for modeling dynamic human bodies. In: ICCV (2021)
Peng, S., et al.: Animatable neural implicit surfaces for creating avatars from videos. arXiv preprint arXiv:2203.08133 (2022)
Peng, S., et al.: Neural body: implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In: CVPR (2021)
Rajeswaran, A., Finn, C., Kakade, S.M., Levine, S.: Meta-learning with implicit gradients. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Remelli, E., et al.: Drivable volumetric avatars using texel-aligned features. In: ACM SIGGRAPH 2022 Conference Proceedings (2022)
Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: PIFu: pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2304–2314 (2019)
Saito, S., Simon, T., Saragih, J., Joo, H.: PIFuHD: multi-level pixel-aligned implicit function for high-resolution 3D human digitization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 84–93 (2020)
Saito, S., Yang, J., Ma, Q., Black, M.J.: SCANimate: weakly supervised learning of skinned clothed avatar networks. In: Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
Shao, R., et al.: FloRen: real-time high-quality human performance rendering via appearance flow using sparse RGB cameras. In: SIGGRAPH Asia 2022 Conference Papers, pp. 1–10 (2022)
Shao, R., et al.: DoubleField: bridging the neural surface and radiance fields for high-fidelity human reconstruction and rendering. In: CVPR (2022)
Shao, R., Zheng, Z., Zhang, H., Sun, J., Liu, Y.: DiffuStereo: high quality human reconstruction via diffusion-based stereo using sparse cameras. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 702–720. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_41
Shen, K., et al.: X-avatar: expressive human avatars. In: Computer Vision and Pattern Recognition (CVPR) (2023)
Shetty, A., Habermann, M., Sun, G., Luvizon, D., Golyanik, V., Theobalt, C.: Holoported characters: real-time free-viewpoint rendering of humans from sparse RGB cameras (2023)
Shuai, Q., et al.: Novel view synthesis of human interactions from sparse multi-view videos. In: SIGGRAPH Conference Proceedings (2022)
Sitzmann, V., Chan, E., Tucker, R., Snavely, N., Wetzstein, G.: MetaSDF: meta-learning signed distance functions. In: Advances in Neural Information Processing Systems, vol. 33, pp. 10136–10147 (2020)
Stoll, C., Hasler, N., Gall, J., Seidel, H.P., Theobalt, C.: Fast articulated motion tracking using a sums of Gaussians body model. In: 2011 International Conference on Computer Vision, pp. 951–958. IEEE (2011)
Su, Z., Xu, L., Zheng, Z., Yu, T., Liu, Y., Fang, L.: RobustFusion: human volumetric capture with data-driven visual cues using a RGBD camera. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part IV. LNCS, vol. 12349, pp. 246–264. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_15
Sun, G., et al.: Neural free-viewpoint performance rendering under complex human-object interactions. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4651–4660 (2021)
Tancik, M., et al.: Learned initializations for optimizing coordinate-based neural representations. In: CVPR (2021)
Tretschk, E., et al.: State of the art in dense monocular non-rigid 3D reconstruction. In: Computer Graphics Forum (Eurographics State of the Art Reports) (2023)
Wang, K., Peng, S., Zhou, X., Yang, J., Zhang, G.: NerfCap: human performance capture with dynamic neural radiance fields. IEEE Trans. Vis. Comput. Graph. 29(12), 5097–5110 (2022)
Wang, K., Zhang, G., Cong, S., Yang, J.: Clothed human performance capture with a double-layer neural radiance fields. In: Computer Vision and Pattern Recognition (CVPR) (2023)
Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: NeuS: learning neural implicit surfaces by volume rendering for multi-view reconstruction. In: NeurIPS (2021)
Wang, S., Mihajlovic, M., Ma, Q., Geiger, A., Tang, S.: MetaAvatar: learning animatable clothed human models from few depth images. In: Advances in Neural Information Processing Systems (2021)
Wang, S., Schwarz, K., Geiger, A., Tang, S.: ARAH: animatable volume rendering of articulated human SDFs. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 1–19. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_1
Wang, Y., Han, Q., Habermann, M., Daniilidis, K., Theobalt, C., Liu, L.: NeuS2: fast learning of neural implicit surfaces for multi-view reconstruction. arXiv preprint arXiv:2212.05231 (2022)
Weng, C.Y., Curless, B., Srinivasan, P.P., Barron, J.T., Kemelmacher-Shlizerman, I.: HumanNeRF: free-viewpoint rendering of moving people from monocular video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16210–16220 (2022)
Xiang, D., et al.: Drivable avatar clothing: faithful full-body telepresence with dynamic clothing driven by sparse RGB-D input. In: SIGGRAPH Asia 2023 Conference Papers, pp. 1–11 (2023)
Xiang, D., Prada, F., Wu, C., Hodgins, J.: MonoClothCap: towards temporally coherent clothing capture from monocular RGB video. In: 2020 International Conference on 3D Vision (3DV), pp. 322–332. IEEE (2020)
Xiu, Y., Yang, J., Cao, X., Tzionas, D., Black, M.J.: ECON: explicit clothed humans optimized via normal integration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 512–523 (2023)
Xiu, Y., Yang, J., Tzionas, D., Black, M.J.: ICON: implicit clothed humans obtained from normals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13296–13306 (2022)
Xu, W., et al.: MonoPerfCap: human performance capture from monocular video. ACM Trans. Graph. 37(2), 27:1–27:15 (2018). https://doi.org/10.1145/3181973
Xue, Y., et al.: NSF: neural surface field for human modeling from monocular depth. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
Yang, J., Pavone, M., Wang, Y.: FreeNeRF: improving few-shot neural rendering with free frequency regularization (2023)
Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: PlenOctrees for real-time rendering of neural radiance fields. In: ICCV (2021)
Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: neural radiance fields from one or few images. In: CVPR (2021)
Yu, T., et al.: DoubleFusion: real-time capture of human performances with inner body shapes from a single depth sensor. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7287–7296 (2018)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA, pp. 586–595. IEEE Computer Society (2018). https://doi.org/10.1109/CVPR.2018.00068. https://doi.ieeecomputersociety.org/10.1109/CVPR.2018.00068
Zhao, F., et al.: Human performance modeling and rendering via neural animated mesh. ACM Trans. Graph. (TOG) 41(6), 1–17 (2022)
Zhao, F., et al.: HumanNeRF: efficiently generated human radiance field from sparse inputs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7743–7753 (2022)
Zheng, Y., et al.: DeepMultiCap: performance capture of multiple characters using sparse multiview cameras. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6239–6249 (2021)
Zheng, Z., Huang, H., Yu, T., Zhang, H., Guo, Y., Liu, Y.: Structured local radiance fields for human avatar modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
Zheng, Z., Yu, T., Liu, Y., Dai, Q.: PaMIR: parametric model-conditioned implicit representation for image-based human reconstruction (2021)
Zheng, Z., Yu, T., Wei, Y., Dai, Q., Liu, Y.: DeepHuman: 3D human reconstruction from a single image. In: The IEEE International Conference on Computer Vision (ICCV) (2019)
Zhu, H., Zhan, F., Theobalt, C., Habermann, M.: TriHuman: a real-time and controllable tri-plane representation for detailed human geometry and appearance synthesis (2023)
Zuo, X., et al.: SparseFusion: dynamic human avatar modeling from sparse RGBD images. IEEE Trans. Multimedia 23, 1617–1629 (2020)
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This research was supported by the ERC Consolidator Grant 4DRepLy (770784).
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Sun, G., Dabral, R., Fua, P., Theobalt, C., Habermann, M. (2025). MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15104. Springer, Cham. https://doi.org/10.1007/978-3-031-72952-2_20
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