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DeepCap: Monocular Human Performance Capture Using Weak Supervision

arXiv 2003.08325 ↗First posted 18 Mar 2020cs.CVcited by 162

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Abstract (arXiv)

Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality. Many previous performance capture approaches either required expensive multi-view setups or did not recover dense space-time coherent geometry with frame-to-frame correspondences. We propose a novel deep learning approach for monocular dense human performance capture. Our method is trained in a weakly supervised manner based on multi-view supervision completely removing the need for training data with 3D ground truth annotations. The network architecture is based on two separate networks that disentangle the task into a pose estimation and a non-rigid surface deformation step. Extensive qualitative and quantitative evaluations show that our approach outperforms the state of the art in terms of quality and robustness.

Key facts. Cited by 162 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 42; 2022: 30; 2023: 40; 2024: 23; 2025: 18; 2026: 9.

162 papers.

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  1. 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 319
  2. 2021Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensorscited by 246
  3. 2022BEHAVE: Dataset and Method for Tracking Human Object Interactionscited by 246
  4. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  5. 2023InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactionscited by 205
  6. 2023GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussianscited by 190
  7. 2021ARCH++: Animation-Ready Clothed Human Reconstruction Revisitedcited by 174
  8. 2023GauHuman: Articulated Gaussian Splatting from Monocular Human Videoscited by 168
  9. 2024OmniRe: Omni Urban Scene Reconstructioncited by 159
  10. 2021H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motioncited by 157
  11. 2022SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Videocited by 144
  12. 2022InstantAvatar: Learning Avatars from Monocular Video in 60 Secondscited by 138
38 more
  1. 2021SPEC: Seeing People in the Wild with an Estimated Cameracited by 134
  2. 2023Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decompositioncited by 134
  3. 2021Real-time Deep Dynamic Characterscited by 133
  4. 2023State of the Art on Diffusion Models for Visual Computingcited by 126
  5. 2023GART: Gaussian Articulated Template Modelscited by 121
  6. 20234DGen: Grounded 4D Content Generation with Spatial-temporal Consistencycited by 117
  7. 2022Structured Local Radiance Fields for Human Avatar Modelingcited by 116
  8. 2022HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modelingcited by 115
  9. 2023TADA! Text to Animatable Digital Avatarscited by 109
  10. 2021Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Controlcited by 106
  11. 2023ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Renderingcited by 104
  12. 2021Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothingcited by 102
  13. 2022CHORE: Contact, Human and Object REconstruction from a single RGB imagecited by 95
  14. 2023MonoHuman: Animatable Human Neural Field from Monocular Videocited by 85
  15. 2023AvatarReX: Real-time Expressive Full-body Avatarscited by 83
  16. 2021Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-Oncited by 81
  17. 2021Neural Monocular 3D Human Motion Capture with Physical Awarenesscited by 70
  18. 2023Learning Neural Volumetric Representations of Dynamic Humans in Minutescited by 70
  19. 2023HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splattingcited by 68
  20. 2021DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Camerascited by 67
  21. 2021Modeling Clothing as a Separate Layer for an Animatable Human Avatarcited by 66
  22. 2021Physics-based Human Motion Estimation and Synthesis from Videoscited by 65
  23. 2022Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representationscited by 65
  24. 2023GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learningcited by 63
  25. 2021SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognitioncited by 59
  26. 2023SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understandingcited by 59
  27. 2022Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Cameracited by 57
  28. 2023MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Capturescited by 54
  29. 2023PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modelingcited by 50
  30. 2023ReFit: Recurrent Fitting Network for 3D Human Recoverycited by 47
  31. 2021Dynamic Surface Function Networks for Clothed Human Bodiescited by 44
  32. 2023Deformable 3D Gaussian Splatting for Animatable Human Avatarscited by 44
  33. 2022LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensorscited by 43
  34. 2021ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generatorscited by 40
  35. 2023High-Fidelity Clothed Avatar Reconstruction from a Single Imagecited by 38
  36. 2022Animatable Implicit Neural Representations for Creating Realistic Avatars from Videoscited by 33
  37. 2022LiDARCap: Long-range Marker-less 3D Human Motion Capture with LiDAR Point Cloudscited by 32
  38. 2023Human101: Training 100+FPS Human Gaussians in 100s from 1 Viewcited by 32

50 most cited of 162 All 162 (JSON)