Zobrazeno 1 - 10
of 731
pro vyhledávání: '"Sitzmann, P."'
Autor:
Li, Sizhe Lester, Zhang, Annan, Chen, Boyuan, Matusik, Hanna, Liu, Chao, Rus, Daniela, Sitzmann, Vincent
Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics. Modern fabrication techniques have dramatically expanded feasible hardware, yet deploying these systems requires control software to
Externí odkaz:
http://arxiv.org/abs/2407.08722
Autor:
Chen, Boyuan, Monso, Diego Marti, Du, Yilun, Simchowitz, Max, Tedrake, Russ, Sitzmann, Vincent
This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next
Externí odkaz:
http://arxiv.org/abs/2407.01392
Skinning is a popular way to rig and deform characters for animation, to compute reduced-order simulations, and to define features for geometry processing. Methods built on skinning rely on weight functions that distribute the influence of each degre
Externí odkaz:
http://arxiv.org/abs/2406.00238
Real-world geometry and 3D vision tasks are replete with challenging symmetries that defy tractable analytical expression. In this paper, we introduce Neural Isometries, an autoencoder framework which learns to map the observation space to a general-
Externí odkaz:
http://arxiv.org/abs/2405.19296
Autor:
Lukoianov, Artem, Borde, Haitz Sáez de Ocáriz, Greenewald, Kristjan, Guizilini, Vitor Campagnolo, Bagautdinov, Timur, Sitzmann, Vincent, Solomon, Justin
While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this discrepancy, we s
Externí odkaz:
http://arxiv.org/abs/2405.15891
This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. Our method performs per-video gradient-descent minimization of a simple least-s
Externí odkaz:
http://arxiv.org/abs/2404.15259
Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques t
Externí odkaz:
http://arxiv.org/abs/2401.13231
We introduce pixelSplat, a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fas
Externí odkaz:
http://arxiv.org/abs/2312.12337
We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Appearance decom
Externí odkaz:
http://arxiv.org/abs/2312.12274
We propose a variational technique to optimize for generalized barycentric coordinates that offers additional control compared to existing models. Prior work represents barycentric coordinates using meshes or closed-form formulae, in practice limitin
Externí odkaz:
http://arxiv.org/abs/2310.03861