37 citations · 76 across the 19 of their papers we have counts for
13 papers · 1 filter
New York Smells: A Large Multimodal Dataset for Olfaction
Ege Ozguroglu, Junbang Liang, Ruoshi Liu +6
While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key bottleneck is the lack of divers…
EraseDraw: Learning to Draw Step-by-Step via Erasing Objects from Images
Alper Canberk, Maksym Bondarenko, Ege Ozguroglu +2
Creative processes such as painting often involve creating different components of an image one by one. Can we build a computational model to perform this task? Prior works often f…
Controlling the World by Sleight of Hand
Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick +2
Humans naturally build mental models of object interactions and dynamics, allowing them to imagine how their surroundings will change if they take a certain action. While generativ…
Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
Basile Van Hoorick, Rundi Wu, Ege Ozguroglu +6
Accurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision. Current dynamic novel view synthesis methods t…
GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering
Abdullah Hamdi, Luke Melas-Kyriazi, Jinjie Mai +5
Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However, it may require a large number of Gaussians, which creates a substant…
pix2gestalt: Amodal Segmentation by Synthesizing Wholes
Ege Ozguroglu, Ruoshi Liu, Dídac Surís +4
We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind o…
Objaverse-XL: A Universe of 10M+ 3D Objects
Matt Deitke, Ruoshi Liu, Matthew Wallingford +14
Natural language processing and 2D vision models have attained remarkable proficiency on many tasks primarily by escalating the scale of training data. However, 3D vision tasks hav…
Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection
Ruoshi Liu, Carl Vondrick
The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources. Since this emitted light has a larger wavelength than visible light, m…
Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
Rundi Wu, Ruoshi Liu, Carl Vondrick +1
Synthesizing novel 3D models that resemble the input example has long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a diffusi…
Zero-1-to-3: Zero-shot One Image to 3D Object
Ruoshi Liu, Rundi Wu, Basile Van Hoorick +3
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this under-constrained settin…
Landscape Learning for Neural Network Inversion
Ruoshi Liu, Chengzhi Mao, Purva Tendulkar +2
Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotic…
Shadows Shed Light on 3D Objects
Ruoshi Liu, Sachit Menon, Chengzhi Mao +3
3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a…
Learning the Predictability of the Future
Dídac Surís, Ruoshi Liu, Carl Vondrick
We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data w…