5 papers
RDM: Recurrent Diffusion Model for Human Motion Generation
Mirgahney Mohamed, Harry Jake Cunningham, Marc P. Deisenroth +1
Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion methods have been proposed due to thei…
AMB3R: Accurate Feed-forward Metric-scale 3D Reconstruction with Backend
Hengyi Wang, Lourdes Agapito
We present AMB3R, a multi-view feed-forward model for dense 3D reconstruction on a metric-scale that addresses diverse 3D vision tasks. The key idea is to leverage a sparse, yet co…
DT-NVS: Diffusion Transformers for Novel View Synthesis
Wonbong Jang, Jonathan Tremblay, Lourdes Agapito
Generating novel views of a natural scene, e.g., every-day scenes both indoors and outdoors, from a single view is an under-explored problem, even though it is an organic extension…
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
We address the 3D reconstruction of human faces from a single RGB image. To this end, we propose Pixel3DMM, a set of highly-generalized vision transformers which predict per-pixel…
Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors
Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy +2
We present Pow3r, a novel large 3D vision regression model that is highly versatile in the input modalities it accepts. Unlike previous feed-forward models that lack any mechanism…