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Found 5 papers, 0 papers with code
DragAPart: Learning a Part-Level Motion Prior for Articulated Objects
We introduce DragAPart, a method that, given an image and a set of drags as input, can generate a new image of the same object in a new state, compatible with the action of the drags.
Learning the 3D Fauna of the Web
We show that prior category-specific attempts fail to generalize to rare species with limited training images.
Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion
We propose a framework that uses an image generator, such as Stable Diffusion, to generate synthetic training data that are sufficiently clean and do not require further manual curation, enabling the learning of such a reconstruction network from scratch.
MagicPony: Learning Articulated 3D Animals in the Wild
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.
Memes in the Wild: Assessing the Generalizability of the Hateful Memes Challenge Dataset
In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset.
