DAMA uses body-anchored Gaussians to reconstruct multi-layered 3D avatars from images, achieving clean garment separation, stacking control, and physical plausibility.
Segment any- thing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2representative citing papers
A two-stage RL method with information gaps and grounding loss trains MLLMs to focus on and precisely crop relevant image regions, yielding SOTA results on high-resolution VQA benchmarks.
citing papers explorer
-
DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars
DAMA uses body-anchored Gaussians to reconstruct multi-layered 3D avatars from images, achieving clean garment separation, stacking control, and physical plausibility.
-
Learning to Focus and Precise Cropping: A Reinforcement Learning Framework with Information Gaps and Grounding Loss for MLLMs
A two-stage RL method with information gaps and grounding loss trains MLLMs to focus on and precisely crop relevant image regions, yielding SOTA results on high-resolution VQA benchmarks.