7 papers
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
Ying Shen, Tianrong Chen, Yuan Gao +6
Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generate interleaved text-image seq…
SimpleFold: Folding Proteins is Simpler than You Think
Yuyang Wang, Jiarui Lu, Navdeep Jaitly +2
Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Noneth…
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
Jiatao Gu, Ying Shen, Tianrong Chen +6
Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Y…
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot +7
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer…
INRFlow: Flow Matching for INRs in Ambient Space
Yuyang Wang, Anurag Ranjan, Josh Susskind +1
Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or…
3D Shape Tokenization via Latent Flow Matching
Jen-Hao Rick Chang, Yuyang Wang, Miguel Angel Bautista Martin +4
We introduce a latent 3D representation that models 3D surfaces as probability density functions in 3D, i.e., p(x,y,z), with flow-matching. Our representation is specifically desig…