arXiv Computer Vision
COSMI: COmpositional Synthesis of Multi-object Interactions
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Sep 30
Harnessing Coupled Stream Completion For Human-Object Interaction Modeling
arXiv:2609.32551v2 Announce Type: replace-cross Abstract: Text-conditioned human-object interaction (HOI) generation requires body motion, object trajectories & rotations, and hand articulation to re...
arXiv Computer Vision
Aug 24
CogCanvas: A Benchmark for Evaluating Multi-Subject Reference-Based Image Generation
CogCanvas is a new benchmark for multi-subject reference-based image generation, featuring 1,952 curated reference images of 100 celebrities, 115 objects/fashion items, and 29 real-world backgrounds. It generates 1,361 compositional prompts with 2–5 people, using a pipeline that includes DINOv2 deduplication, aesthetic filtering, and automated graph derivation for interaction and positioning. The benchmark evaluates three tasks—reference-based multi-human-object generation, text-to-image compositional generation, and reference retrieval—under a six-axis protocol, and introduces BG‑Sim and Attr‑VQA metrics to assess background fidelity and attribute binding.
Hugging Face Trending Papers
Jul 8
GIRAF: Towards Generalizable Human Interactions with Articulated Objects
Synthesizing realistic full-body human interactions with articulated objects is a fundamental challenge for embodied AI and graphics, with applications in robotics training and virtual agents. Existing models remain limited: some focus on simple activities with static objects, while others restrict attention to hand-only manipulation.
Hugging Face Trending Papers
6d ago
ECHO: Embodied Camera Observations of Human Object Carrying
Embodied and assistive agents must do more than recognize objects: they must reason about where an object belongs given the layout of an environment and the habits of the people who live in it. Progre...
arXiv AI
Jun 12
M*: A Modular, Extensible, Serving System for Multimodal Models
arXiv:2606. 12688v1 Announce Type: cross Abstract: We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors.
arXiv Computer Vision
Sep 28
Timo: $\textbf{T}$aming Mult$\textbf{i}$modal Diffusion Transformer for Human $\textbf{Mo}$tion Generation
The paper introduces Timo, a kinematics-aware multimodal diffusion transformer designed for human motion generation. Timo employs fully shared multimodal attention, flow matching, and geometric/rotational-kinematics supervision to better coordinate articulated motion, and uses a two-stage curriculum to align motion with text captions. The authors also present a new benchmark of 40,025 clips from six datasets, showing that Timo outperforms state‑of‑the‑art methods, achieving a 40.8% relative improvement over Kimodo on average.