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Found 16 papers, 5 papers with code
Differentiable Robot Rendering
Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings.
EraseDraw: Learning to Draw Step-by-Step via Erasing Objects from Images
With this scalable automatic data generation pipeline, we can create a dataset for learning object insertion, which is used to train our proposed text conditioned diffusion model.
Controlling the World by Sleight of Hand
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.
Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
A key challenge in manipulation is learning a policy that can robustly generalize to diverse visual environments.
Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
Accurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision.
GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering
With the aid of a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing the rendering speed by up to 39%.
pix2gestalt: Amodal Segmentation by Synthesizing Wholes
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 occlusions.
GES : Generalized Exponential Splatting for Efficient Radiance Field Rendering
With the aid of a frequency-modulated loss GES achieves competitive performance in novel-view synthesis benchmarks while requiring less than half the memory storage of Gaussian Splatting and increasing the rendering speed by up to 39%.
Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape
Specifically, we encode the input 3D textured shape into triplane feature maps that represent the signed distance and texture fields of the input.
Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection
The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources.
Zero-1-to-3: Zero-shot One Image to 3D Object
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
What You Can Reconstruct From a Shadow
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Landscape Learning for Neural Network Inversion
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, robotics, and graphics.
Shadows Shed Light on 3D Objects
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Learning the Predictability of the Future
We introduce a framework for learning from unlabeled video what is predictable in the future.
Representation Learning
Self-Supervised Action Recognition
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Detecting Topological Defects in 2D Active Nematics Using Convolutional Neural Networks
Active matter consists of active agents which transform energy extracted from surroundings into momentum, producing a variety of collective phenomena.
