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Found 8 papers, 3 papers with code
ZeST: Zero-Shot Material Transfer from a Single Image
We propose ZeST, a method for zero-shot material transfer to an object in the input image given a material exemplar image.
See, Imagine, Plan: Discovering and Hallucinating Tasks from a Single Image
Humans can not only recognize and understand the world in its current state but also envision future scenarios that extend beyond immediate perception.
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e. g., LAION).
Learning Continuous 3D Words for Text-to-Image Generation
We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words.
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets.
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation
A truly generalizable approach to rigid segmentation and motion estimation is fundamental to 3D understanding of articulated objects and moving scenes.
Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds
In this work, we propose an almost-universal sampler, in our quest for a sampler that can learn to preserve the most useful points for a particular task, yet be inexpensive to adapt to different tasks, models, or datasets.
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction.
