3 citations · 12 across the 8 of their papers we have counts for
8 papers
InteractGesture: Progressive Chunk Guidance for Continuous Streaming Co-Speech Gesture Control
Ekkasit Pinyoanuntapong, Ajinkya Deogade, Paul Streli +5
Co-speech gesture generation has made significant progress toward realistic full-body motion from speaker audio, yet existing models lack fine-grained spatial controllability of in…
Opt-In Art: Learning Art Styles Only from Few Examples
Hui Ren, Joanna Materzynska, Rohit Gandikota +3
We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investigate this, we train a text-to-ima…
NewMove: Customizing text-to-video models with novel motions
Joanna Materzynska, Josef Sivic, Eli Shechtman +3
We introduce an approach for augmenting text-to-video generation models with customized motions, extending their capabilities beyond the motions depicted in the original training d…
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
Rohit Gandikota, Joanna Materzynska, Tingrui Zhou +2
We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models. Our approach identifies a low-ra…
FIND: A Function Description Benchmark for Evaluating Interpretability Methods
Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska +5
Labeling neural network submodules with human-legible descriptions is useful for many downstream tasks: such descriptions can surface failures, guide interventions, and perhaps eve…
Unified Concept Editing in Diffusion Models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov +2
Text-to-image models suffer from various safety issues that may limit their suitability for deployment. Previous methods have separately addressed individual issues of bias, copyri…
Erasing Concepts from Diffusion Models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman +1
Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing e…
Disentangling visual and written concepts in CLIP
Joanna Materzynska, Antonio Torralba, David Bau
The CLIP network measures the similarity between natural text and images; in this work, we investigate the entanglement of the representation of word images and natural images in i…