mlsanityData to 7 Oct 2026

ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics

arXiv 2106.01981 ↗First posted 3 Jun 2021cs.CV cs.GR cs.LGcited by 17

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Abstract (arXiv)

Our work focuses on the development of a learnable neural representation of human pose for advanced AI assisted animation tooling. Specifically, we tackle the problem of constructing a full static human pose based on sparse and variable user inputs (e.g. locations and/or orientations of a subset of body joints). To solve this problem, we propose a novel neural architecture that combines residual connections with prototype encoding of a partially specified pose to create a new complete pose from the learned latent space. We show that our architecture outperforms a baseline based on Transformer, both in terms of accuracy and computational efficiency. Additionally, we develop a user interface to integrate our neural model in Unity, a real-time 3D development platform. Furthermore, we introduce two new datasets representing the static human pose modeling problem, based on high-quality human motion capture data, which will be released publicly along with model code.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 22 of its 52 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

22 of 52 references.

References · 22

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10 more
  1. 2021Robust Motion In-betweeningcited by 223
  2. 2018QuaterNet: A Quaternion-based Recurrent Model for Human Motioncited by 108
  3. 2021LoBSTr: Real-time Lower-body Pose Prediction from Sparse Upper-body Tracking Signalscited by 45
  4. 2018Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registrationcited by 40
  5. 2016Space-Time Representation of People Based on 3D Skeletal Data: A Reviewcited by 24
  6. 2020Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videoscited by 23
  7. 2021FixMyPose: Pose Correctional Captioning and Retrievalcited by 12
  8. 2013Classifying and Visualizing Motion Capture Sequences using Deep Neural Networkscited by 3
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  10. 2020AnimGAN: A Spatiotemporally-Conditioned Generative Adversarial Network for Character Animationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 5; 2025: 3; 2026: 2.

17 papers.

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  1. 2023NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimationcited by 45
  2. 2023BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesiscited by 44
  3. 2024HanDiffuser: Text-to-Image Generation With Realistic Hand Appearancescited by 42
  4. 2023Iterative Motion Editing with Natural Languagecited by 39
  5. 2023PoseFix: Correcting 3D Human Poses with Natural Languagecited by 38
  6. 2022Pretrained Diffusion Models for Unified Human Motion Synthesiscited by 26
  7. 2025SimMotionEdit: Text-Based Human Motion Editing with Motion Similarity Predictioncited by 26
  8. 2024Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modelingcited by 7
  9. 2024Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agentscited by 7
  10. 2022SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflowscited by 5
  11. 2023CycleIK: Neuro-inspired Inverse Kinematicscited by 5
  12. 2026Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learningcited by 4
5 more
  1. 2024DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimizationcited by 3
  2. 2025Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematicscited by 2
  3. 2024MODL: Multilearner Online Deep Learningcited by 1
  4. 2026Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editingcited by 1
  5. 2025Probabilistic Pretraining for Neural Regressioncited by 0