mlsanityData to 7 Oct 2026

Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model

arXiv 2010.09950 ↗First posted 20 Oct 2020cs.GRcited by 16

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Abstract (arXiv)

We present a probabilistic framework to generate character animations based on weak control signals, such that the synthesized motions are realistic while retaining the stochastic nature of human movement. The proposed architecture, which is designed as a hierarchical recurrent model, maps each sub-sequence of motions into a stochastic latent code using a variational autoencoder extended over the temporal domain. We also propose an objective function which respects the impact of each joint on the pose and compares the joint angles based on angular distance. We use two novel quantitative protocols and human qualitative assessment to demonstrate the ability of our model to generate convincing and diverse periodic and non-periodic motion sequences without the need for strong control signals.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 2; 2023: 3; 2024: 4; 2025: 0; 2026: 1.

16 papers.

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  1. 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 319
  2. 2023Single Motion Diffusioncited by 65
  3. 2022MoDi: Unconditional Motion Synthesis from Diverse Datacited by 59
  4. 2024Estimating Body and Hand Motion in an Ego-sensed Worldcited by 56
  5. 2021Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structurecited by 53
  6. 2024Dyn-HaMR: Recovering 4D Interacting Hand Motion from a Dynamic Cameracited by 50
  7. 2021GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interactioncited by 18
  8. 2021TrajeVAE: Controllable Human Motion Generation from Trajectoriescited by 14
  9. 2021Graph-based Normalizing Flow for Human Motion Generation and Reconstructioncited by 12
  10. 20234DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacementscited by 12
  11. 2024LEAD: Latent Realignment for Human Motion Diffusioncited by 9
  12. 2024Learned Neural Physics Simulation for Articulated 3D Human Pose Reconstructioncited by 6
4 more
  1. 2021A structured latent space for human body motion generationcited by 5
  2. 2022Representing motion as a sequence of latent primitives, a flexible approach for human motion modellingcited by 2
  3. 2023Diverse Motion In-betweening with Dual Posture Stitchingcited by 2
  4. 2026Context-Aware Autoregressive Diffusion for Gloss-Wise Sign Language Productioncited by 1