mlsanityData to 14 Oct 2026

Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes

arXiv 2410.10790 ↗First posted 14 Oct 2024cs.CVcited by 14

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Abstract (arXiv)

Recent advancements in human motion synthesis have focused on specific types of motions, such as human-scene interaction, locomotion or human-human interaction, however, there is a lack of a unified system capable of generating a diverse combination of motion types. In response, we introduce Sitcom-Crafter, a comprehensive and extendable system for human motion generation in 3D space, which can be guided by extensive plot contexts to enhance workflow efficiency for anime and game designers. The system is comprised of eight modules, three of which are dedicated to motion generation, while the remaining five are augmentation modules that ensure consistent fusion of motion sequences and system functionality. Central to the generation modules is our novel 3D scene-aware human-human interaction module, which addresses collision issues by synthesizing implicit 3D Signed Distance Function (SDF) points around motion spaces, thereby minimizing human-scene collisions without additional data collection costs. Complementing this, our locomotion and human-scene interaction modules leverage existing methods to enrich the system's motion generation capabilities. Augmentation modules encompass plot comprehension for command generation, motion synchronization for seamless integration of different motion types, hand pose retrieval to enhance motion realism, motion collision revision to prevent human collisions, and 3D retargeting to ensure visual fidelity. Experimental evaluations validate the system's ability to generate high-quality, diverse, and physically realistic motions, underscoring its potential for advancing creative workflows. Project page: https://windvchen.github.io/Sitcom-Crafter.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

35 of 45 references.

References · 35

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  12. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 368
23 more
  1. 2023Human Motion Diffusion as a Generative Priorcited by 357
  2. 2017On human motion prediction using recurrent neural networkscited by 338
  3. 2020Monocular, One-stage, Regression of Multiple 3D Peoplecited by 218
  4. 2023Object Motion Guided Human Motion Synthesiscited by 212
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  6. 2023InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusioncited by 182
  7. 2017HP-GAN: Probabilistic 3D human motion prediction via GANcited by 176
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  9. 2020Populating 3D Scenes by Learning Human-Scene Interactioncited by 164
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  11. 2019Generating 3D People in Scenes without Peoplecited by 127
  12. 2023Synthesizing Diverse Human Motions in 3D Indoor Scenescited by 109
  13. 2023Inter-X: Towards Versatile Human-Human Interaction Analysiscited by 98
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  15. 2018MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamicscited by 86
  16. 2022Compositional Human-Scene Interaction Synthesis with Semantic Controlcited by 83
  17. 2019You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactionscited by 82
  18. 2019Putting Humans in a Scene: Learning Affordance in 3D Indoor Environmentscited by 72
  19. 2021The Wanderings of Odysseus in 3D Scenescited by 59
  20. 2023Generative Proxemics: A Prior for 3D Social Interaction from Imagescited by 43
  21. 2023Digital Life Project: Autonomous 3D Characters with Social Intelligencecited by 43
  22. 2023Generating Continual Human Motion in Diverse 3D Scenescited by 39
  23. 2022Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 4.

14 papers.

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