Harnessing Coupled Stream Completion For Human-Object Interaction Modeling

arXiv:2609.32551 · cs.CV, cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-26

Updated: 2026-09-29

Project page: https://visdyn.github.io/TRACE

Terminology

• Trace — 40× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Consistency — 8× in this paper · explained in 3 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Joint Attention — 7× in this paper · explained in 2 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Adaptation — 5× in this paper · explained in 1 episode
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Affordance — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Eleventh International Conference on Learning Representations — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Text-conditioned Human-Object Interaction — 2× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling
• Auto-Encoding Variational Bayes
• LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning
• Large Language Diffusion Models
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
• Uni-HOI:A Unified framework for Learning the Joint distribution of Text and Human-Object Interaction
• OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation Beyond Training Domains

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