1 citations · 2 across the 11 of their papers we have counts for
12 papers
Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control
Ruining Li, Yuxin Yao, Matt Zhou +5
Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but…
Articraft: An Agentic System for Scalable Articulated 3D Asset Generation
Matt Zhou, Ruining Li, Xiaoyang Lyu +6
A bottleneck in learning to understand articulated 3D objects is the lack of large and diverse datasets. In this paper, we propose to leverage large language models (LLMs) to close…
Particulate: Feed-Forward 3D Object Articulation
Ruining Li, Yuxin Yao, Chuanxia Zheng +4
We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion const…
On Vanishing Variance in Transformer Length Generalization
Ruining Li, Gabrijel Boduljak, Jensen +1
It is a widely known issue that Transformers, when trained on shorter sequences, fail to generalize robustly to longer ones at test time. This raises the question of whether Transf…
DSO: Aligning 3D Generators with Simulation Feedback for Physical Soundness
Ruining Li, Chuanxia Zheng, Christian Rupprecht +1
Most 3D object generators prioritize aesthetic quality, often neglecting the physical constraints necessary for practical applications. One such constraint is that a 3D object shou…
DreamHOI: Subject-Driven Generation of 3D Human-Object Interactions with Diffusion Priors
Thomas Hanwen Zhu, Ruining Li, Tomas Jakab
We present DreamHOI, a novel method for zero-shot synthesis of human-object interactions (HOIs), enabling a 3D human model to realistically interact with any given object based on…
Puppet-Master: Scaling Interactive Video Generation as a Motion Prior for Part-Level Dynamics
Ruining Li, Chuanxia Zheng, Christian Rupprecht +1
We introduce Puppet-Master, an interactive video generator that captures the internal, part-level motion of objects, serving as a proxy for modeling object dynamics universally. Gi…
DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
Ruining Li, Chuanxia Zheng, Christian Rupprecht +1
We introduce DragAPart, a method that, given an image and a set of drags as input, generates a new image of the same object that responds to the action of the drags. Differently fr…
Learning the 3D Fauna of the Web
Zizhang Li, Dor Litvak, Ruining Li +6
Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan…
Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion
Tomas Jakab, Ruining Li, Shangzhe Wu +2
We present Farm3D, a method for learning category-specific 3D reconstructors for articulated objects, relying solely on "free" virtual supervision from a pre-trained 2D diffusion-b…
MagicPony: Learning Articulated 3D Animals in the Wild
Shangzhe Wu, Ruining Li, Tomas Jakab +2
We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and lighting of an articulated animal like a horse given a single test image as input. We pres…
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
Hannah Rose Kirk, Yennie Jun, Paulius Rauba +7
Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released…