HumanSkill-4D: Training-Free Language Querying of Animatable Gaussian Avatars in Space and Time

Key points

• A query can also specify a pose or motion to select matching intervals.1
• We address these challenges with HumanSkill-4D, a training-free framework that shares a multimodal large language model (MLLM) and geometric operators across avatars.1
• For each avatar, a semantic memory links body-part and garment categories to persistent Gaussian regions and appearance descriptions.1
• To evaluate this task, we introduce three human-annotated benchmarks and show that HumanSkill-4D outperforms the baselines in part segmentation and 3D and 4D referring segmentation, with compact memories and only minutes of setup per avatar.1

Sources (2)

• [1]HumanSkill-4D: Training-Free Language Querying of Animatable Gaussian Avatars in Space and Time
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 04:43 AM

  We introduce avatar referring segmentation, which selects regions of animatable Gaussian avatars from language queries.

  A query can also specify a pose or motion to select matching intervals.

• [2]HumanSkill-4D: Training-Free Language Querying of Animatable Gaussian Avatars in Space and Time
  Hugging Face Daily Papers · Oct 17, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.