The paper introduces DORS, a diffusion-based framework for removing objects from dense scenes. It employs Dynamic Attention Routing, combining Instance-Filtered Attention to block misleading semantic cues from similar objects and Context-Guided Routing to preserve visual consistency. A new benchmark, DOR-Bench, is also presented, and experiments show DORS outperforms existing methods in reducing incomplete removal and duplicate artifacts.
arXiv:2606. 28094v1 Announce Type: cross Abstract: Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the fact that user-provided masks are often inaccurate or incomplete.
arXiv:2608. 20107v1 Announce Type: new Abstract: Recent advances in generative video models have significantly improved visual realism in video object removal, yet evaluation protocols still focus on masked region fidelity, treating removal as local inpainting.
arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...
This paper presents a zero‑shot framework for removing objects from real images using a frozen Stable Diffusion model, avoiding any task‑specific training. The pipeline combines SAM‑based mask construction, BLIP caption conditioning, DDIM inversion, background‑weighted masked null‑text optimization, decoder self‑attention masking, hard outside‑mask latent anchoring, and localized renoise‑denoise refinement. Experiments show effective removal of objects and context‑consistent replacement, with background‑weighted NTI especially helpful for complex backgrounds and repeated refinement reducing residual artifacts.
Removing an object from a real image requires more than synthesizing plausible content within a mask: the method must suppress residual object features, preserve the unedited scene, and generate repla...
DiDA introduces a lightweight video object segmentation framework that leverages Distillation Learning of Deformable Attention. The method uses deformable attention to adapt key and value positions across frames, enabling object representations that are responsive to spatial and temporal changes. Experiments on DAVIS and YouTube‑VOS benchmarks show state‑of‑the‑art performance and efficient memory usage.
arXiv:2609.13318v1 Announce Type: cross Abstract: 3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tig...
arXiv:2606. 27584v1 Announce Type: cross Abstract: 3D scene inpainting is essential for reconstructing areas corrupted by occlusions or limited viewpoints.
The paper introduces Text-to-Seed (T2S), a training‑free framework for open‑vocabulary semantic segmentation that repurposes Stable Diffusion to generate attention‑based seed points from text queries. These sparse seeds serve as point prompts for the Segment Anything Model (SAM), enabling reliable region expansion without relying on inaccurate coarse masks. T2S achieves strong performance on standard OVSS benchmarks using only the text‑to‑region correspondence of diffusion models and no task‑specific training or extra annotations.
arXiv:2608.22064v1 Announce Type: new Abstract: We present our solution for the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. The challenge evaluates r...
FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.
The paper introduces ShadowCLR, an unsupervised framework for removing shadows from images without requiring paired shadow–shadow-free data or shadow masks. By leveraging consistency across multiple shadowed observations of the same scene, the method regularizes the model to recover scene-consistent appearance while suppressing shadow-specific variations. Experiments on several benchmarks show that ShadowCLR achieves competitive or superior performance compared to existing unsupervised approaches.