OccluRank: Controllable Occlusion-Aware Layout-to-Image Generation by Adding Just an Ordinal Rank
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2610.09071v1 Announce Type: new Abstract: Layout-to-Image generation has made substantial progress in spatial and object-level control. However, existing methods still struggle with complex sce...
Layout-to-Image generation has made substantial progress in spatial and object-level control. However, existing methods still struggle with complex scenes containing many overlapping and interacting o...
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: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...
FreeInpaint is a feed‑forward 3D inpainting framework that reconstructs complete, geometrically consistent scenes directly from unposed multi‑view images with masked regions. It extends a 3D foundation model by propagating masked areas across views, using a Learnable Mask Attention mechanism to maintain reliable cross‑view correspondences and a Support Token Refinement strategy that injects diffusion‑generated auxiliary tokens for high‑fidelity completion. Experiments on diverse datasets show that FreeInpaint delivers superior inpainting quality without requiring pre‑computed camera poses, while maintaining fast inference speed.
LIFT is a unified image‑to‑video generation framework that adds Layout‑In‑Future control, letting users specify what should appear and where in a future view. It addresses the limitation of existing camera controls and text prompts by using the last‑frame layout as an explicit signal for the desired future scene, especially under large viewpoint changes. To handle sparse layout guidance, LIFT employs on‑policy self‑distillation to transfer knowledge from a dense‑layout teacher to a last‑frame‑layout student, and introduces the LIFT‑Vista dataset with large viewpoint changes and consistent layout annotations. Experiments demonstrate that LIFT improves video quality, future‑layout controllability, and camera controllability compared to other methods.