arXiv Computer Vision
SceneOrchestra: Efficient Agentic 3D Scene Synthesis via Full Tool-Call Trajectory Generation
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VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System
arXiv:2607. 27380v2 Announce Type: replace-cross Abstract: Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be inferred implicitly from a highly compressed text prompt.
arXiv AI
Jun 16
SceneConductor: 3D Scene Generation from a Single Image with Multi-Agent Orchestration
arXiv:2606. 08402v2 Announce Type: replace-cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence.
arXiv AI
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FRAMEWORKERS: A Dynamic Multi-Agent Framework for AI-Generated Video Production
FRAMEWORKERS is a task‑centric, multi‑agent framework designed for end‑to‑end AI‑generated video production. It uses a central Director to dynamically manage a task stack and an Assistant to execute tasks within a shared Workspace, leveraging modular sub‑agents that can be added without redesigning the workflow. The system is fine‑tuned with supervised learning and policy optimization, outperforming existing LLM planners and fixed pipelines in routing accuracy, failure recovery, and overall video quality.
arXiv AI
Jun 9
SceneConductor: 3D Scene Generation from Single Image with Multi-Agent Orchestration
arXiv:2606. 08402v1 Announce Type: cross Abstract: Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence.
arXiv AI
Jun 2
3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
arXiv:2606. 01057v1 Announce Type: cross Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently lack.
arXiv Computer Vision
Sep 7
WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing
WeAgent-MMGenEdit is a comprehensive framework for multimodal agentic image generation and editing that addresses the unreliability of current models when prompts require external world knowledge. It introduces a multimodal harness with persistent evidence management, a scalable data construction pipeline producing 23K supervised trajectories and 14.7K RL tasks, and a bilingual benchmark (WeBench-MMGenEdit) for knowledge-intensive generation and multi-image editing. Post‑training methods based on SFT and RL further refine the agent policy and image backend, enabling a 30B‑parameter policy to outperform similarly sized models and approach the performance of a 1T‑parameter agent.