arXiv AI
POCI-Diff: 3D-Layout Guided Diffusion for Controllable Synthetic Surveillance Data Generation
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arXiv Computer Vision
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Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction
arXiv:2609.23796v2 Announce Type: replace Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open ch...
arXiv Computer Vision
Sep 22
Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene
arXiv:2609.23796v1 Announce Type: new Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challe...
arXiv AI
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Interact3D: Compositional 3D Generation of Interactive Objects
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Hugging Face Trending Papers
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LooseControlVideo: Directorial Video Control using Spatial Blocking
Precise 3D spatial orchestration in text-to-video generation remains a significant challenge, particularly for multi-object scenes where semantic layout and temporal dynamics are often entangled. While existing depth-conditioned models achieve good structural fidelity, they necessitate dense, frame-accurate guidance that is labor-intensive to author for dynamic events involving deformable objects.
arXiv Computer Vision
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CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling
CameraEditor is a new framework that transforms camera-controlled image editing into a temporal sequence prediction problem. By using video diffusion models, it incorporates a geometric perception module and dynamic reference routing to create precise visual references through dynamic panorama cropping. The method also inserts intermediate transition frames to handle large perspective shifts, maintaining content identity and spatial coherence, and is evaluated on a dataset of 5,760 instances with a benchmark of 462 test cases, achieving state‑of‑the‑art performance.
arXiv Machine Learning
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AccioScene: Compositional 3D Scene Generation via Graph Diffusion and Interaction-driven Critics
arXiv:2502. 06819v2 Announce Type: replace Abstract: This paper presents a framework for generating 3D indoor scenes from text prompts.