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Miguel Ángel Bautista

14 accepted papers

2026

Adapting Self-Supervised Representations as a Latent Space for Efficient Generation

Ming Gui, Johannes Schusterbauer, Timy Phan, Felix Krause, Joshua M. Susskind, Miguel Ángel Bautista +1
ICLR 2026poster

We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision transformers. Building on a pre-trained SSL encoder, we fine-tune only the semantic token embedding and pair it with a…

Cited by 0Source ↗code ↗Scholar ↗
2026

Learning Long-term Motion Embeddings for Efficient Kinematics Generation

Nick Stracke, Kolja Bauer, Stefan Andreas Baumann, Miguel Ángel Bautista, Josh Susskind, Björn Ommer
CVPR 2026

Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multiple possible futures through full video synthesis remains prohibitively inefficient. We model scene dynamics orders of ma

Cited by 0Source ↗code ↗Scholar ↗
2026

SimpleFold: Folding Proteins is Simpler than You Think

Yuyang Wang, Jiarui Lu, Navdeep Jaitly, Joshua M. Susskind, Miguel Ángel Bautista
ICLR 2026poster

Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether t…

Cited by 0Source ↗code ↗Scholar ↗
2025

Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows

Ruixiang ZHANG, Shuangfei Zhai, Jiatao Gu, Yizhe Zhang, Huangjie Zheng, Tianrong Chen +3
NeurIPS 2025poster

Autoregressive models have driven remarkable progress in language modeling. Their foundational reliance on discrete tokens, unidirectional context, and single-pass decoding, while central to their success, also inspires the exploration of a design space that could offer new axes of modeling flexibil…

Cited by 0Source ↗Scholar ↗
2025

INRFlow: Flow Matching for INRs in Ambient Space

Yuyang Wang, Anurag Ranjan, Joshua M. Susskind, Miguel Ángel Bautista
ICML 2025poster

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 even protein structures. These models are commonly trained in two stages: first, a data compressor is trained, and in a…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Normalizing Flows are Capable Generative Models

Shuangfei Zhai, Ruixiang ZHANG, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng +4
ICML 2025oral

Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than pr…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2025

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

Jiatao Gu, Tianrong Chen, David Berthelot, Huangjie Zheng, Yuyang Wang, Ruixiang ZHANG +4
NeurIPS 2025spotlight

We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance on high-resolution image synthesis. STARFlow's main building block is Transformer Autoregressive Flow (TARFlow), which combines normalizing flows with Autoregressive Transformer architecture…

Cited by 0Source ↗code ↗Scholar ↗
2024

Pseudo-Generalized Dynamic View Synthesis from a Video

Xiaoming Zhao, R Alex Colburn, Fangchang Ma, Miguel Ángel Bautista, Joshua M. Susskind, Alex Schwing
ICLR 2024poster

Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized techniques, which only run a deep net forward pass on a test sce…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2024

Scalable Pre-training of Large Autoregressive Image Models

Alaaeldin El-Nouby, Michal Klein, Shuangfei Zhai, Miguel Ángel Bautista, Vaishaal Shankar, Alexander T Toshev +2
ICML 2024poster

This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their textual counterparts, i.e., Large Language Models (LLMs), and exhibit similar scaling properties. Specifically, we highlight two key findings: (1) the performance…

Cited by 70Source ↗PDF ↗code ↗Scholar ↗
2024

Swallowing the Bitter Pill: Simplified Scalable Conformer Generation

Yuyang Wang, Ahmed A. A. Elhag, Navdeep Jaitly, Joshua M. Susskind, Miguel Ángel Bautista
ICML 2024poster

We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by using the advantages of scale. By training a diffusion generative model directly on 3D atomic positions without making ass…

Cited by 17Source ↗PDF ↗Scholar ↗
2023

Diffusion Probabilistic Fields

Peiye Zhuang, Samira Abnar, Jiatao Gu, Alex Schwing, Joshua M. Susskind, Miguel Ángel Bautista
ICLR 2023poster

Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes…

Cited by 28Source ↗PDF ↗Scholar ↗
2023

f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation

Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Ángel Bautista, Joshua M. Susskind
ICLR 2023poster

Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational autoencoders (VAEs) where the latent variables are inferred from input-centered Gaussian distributions with fixed scales a…

Cited by 28Source ↗PDF ↗Scholar ↗
2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

Miguel Ángel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander T Toshev, Zhuoyuan Chen +6
NeurIPS 2022accept

We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disent…

Cited by 142Source ↗PDF ↗code ↗Scholar ↗