Affostruction reconstructs full 3D object geometry from partial RGBD views and grounds text-based affordances on both visible and unobserved surfaces, reporting large gains over prior methods.
Flow matching for genera- tive modeling
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MaTe proposes a training-free diffusion transformer that performs material transfer using only images by integrating them at the token level for unified multi-modal attention in a shared latent space.
citing papers explorer
-
Affostruction: 3D Affordance Grounding with Generative Reconstruction
Affostruction reconstructs full 3D object geometry from partial RGBD views and grounds text-based affordances on both visible and unobserved surfaces, reporting large gains over prior methods.
-
MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer
MaTe proposes a training-free diffusion transformer that performs material transfer using only images by integrating them at the token level for unified multi-modal attention in a shared latent space.