Variational Autoencoders

Also known as VAE

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10 papers in the last four weeks, up 25% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 124

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MeshCarve: Artisan Mesh Generation with Flow Matching in Compact Latent Spaces
Oct 7, 2026Xiyu Wang, Ruocheng Wu, Yufei Wang +33D Mesh Generation Flow-Based Generative Modeling
Nanyang Technological University · SparAI Inc. · University of Texas at Austin
Feature Encoding in VAE-based Audio Decoders: Effects of Input, Depth and Distribution
Oct 6, 2026Louis McCallum, Mick GriersonVariational Autoencoders Audio Representation Learning
Creative Computing Institute, University of the Arts London, London, UK
PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
Oct 6, 2026Yu Zhang, Xudong Xu, Xingang PanVariational Autoencoders Latent Diffusion Models
S-Lab, Nanyang Technological University, Singapore · Shanghai Artificial Intelligence Laboratory, Shanghai, China
Learning Disentangled Representations with Quantum Variational Autoencoders
Oct 5, 2026Gaoyuan Wang, Jerry Tan, Mark GersteinVariational Autoencoders Disentangled Representation Learning
Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA · Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA · Department of Computer Science, Yale University, New Haven, Connecticut 06520, USA +2
Empirical Variational Autoencoder
Oct 5, 2026Kaede ShioharaVariational Autoencoders Latent Variable Models
The University of Tokyo
Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation
Oct 5, 2026Álvaro Díez, Fidel AznarVariational Autoencoders Domain Adaptation
Department of Computer Science and Artificial Intelligence, University of Alicante
On Color Alignment in VAE Latent Spaces and Its Applications
Oct 5, 2026Julian D. Santamaria, Kai Wang, Jesús Malo +2Variational Autoencoders Image Generation
Computer Vision Center · Universitat Aut`onoma de Barcelona · City University of Hong Kong (Dongguan) +1
scTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning
Sep 30, 2026Yunhak Oh, Yoonho Lee, Junseok Lee +9Variational Autoencoders Representation Learning
KAIST · Work done during an internship at HITS · HITS
A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling
Sep 29, 2026Minglei Yang, Marshall Nicholson, Diego Del-Castillo-Negrete +2Variational Autoencoders Physics-Informed Generative Modeling
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Grand Valley State University, Allendale, MI, 49401, USA · University of Texas at Austin, Austin, TX, 78712, USA
Continuous Variational Synthesis
Sep 28, 2026Alan N. Amin, Mattia G. Gollub, Andrei Slabodkin +2Variational Autoencoders Constrained Generative Modeling
JURA Bio, Inc. Boston, MA 02125, USA
HALO: Enhancing Time Series Generation via Hyperspherical Latents and Masked AutoregRessive Modeling
Sep 28, 2026Chunyi Hou, Xiangfei Qiu, Hanyin Cheng +2Variational Autoencoders Time Series Generation
School of Data Science and Engineering, East China Normal University, Shanghai, China
Learning Holographic Reduced Representations with Clifford Variational Autoencoders
Sep 23, 2026Mohamed Malek Abid, P. Michael FurlongVariational Autoencoders Visual Representation Learning
Institute of Neuroinformatics, University of Zurich & ETH Zurich · National Research Council Canada & Centre for Theoretical Neuroscience, Systems Design Engineering, University of Waterloo, Waterloo, Canada
CFD Correction of Open Tip Clearance Flow in a Compressor Cascade Using VAE Latent Space Adaptation
Sep 23, 2026Xiang Zuo, Hefang Deng, Caiyan Chen +4Variational Autoencoders Computational Fluid Dynamics
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China · Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Stable by Construction: Variational Latent Markov Operators for Long-Horizon PDE Prediction
Sep 15, 2026Junyi Liao, Johann Guilleminot, Vahid TarokhVariational Autoencoders Latent Dynamics Modeling
†Department of Electrical and Computer Engineering, Duke University · ‡Department of Mechanical Engineering and Materials Science, Duke University
Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
Sep 12, 2026Angela Cratere, Luca Ghilardi, Vishnu Reddy +3Variational Autoencoders Small Object Detection
Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands · Systems and Industrial Engineering, University of Arizona, Tucson, USA · Space4 Center, University of Arizona, Tucson, USA +2
LoopVAE: Recurrent Depth Across Scales for Visual Tokenization
Sep 11, 2026Zhiying LuVariational Autoencoders Autoencoders
University of Science and Technology of China
Deformable Object Manipulation under Partial Observability via Real-Time Full-Shape Estimation
Sep 9, 2026Kosar Behnia, Ville Kyrki, Gokhan AlcanVariational Autoencoders Deformable Object Manipulation
Faculty of Engineering and Natural Sciences, Tampere Univ., Finland. · Electrical Engineering and Automation Department, Aalto Univ., Finland.
Learning Sparse Decision Trees via Transformer Variational Auto-Encoders
Sep 1, 2026Giacomo Fidone, Alessio Cascione, Riccardo GuidottiVariational Autoencoders Structured Sparsity
University of Pisa · ISTI-CNR
XVAE-WMT: Explainable Wavelet-Temporal Variational Autoencoder for Blind Source Separation of Heart and Lung Sounds
Aug 31, 2026Yasaman Torabi, Shahram Shirani, James P. ReillyVariational Autoencoders Audio Source Separation
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada. · L.R. Wilson/Bell Canada Chair in Data Communications, Hamilton, ON L8S 4L7, Canada.
A Deep Latent Variable Framework for Jointly Modeling Missingness, Measurement Error, and Heterogeneity
Aug 30, 2026Yasin Khadem Charvadeh, Grace Y. Yi, Mithat Gönen +1Variational Autoencoders Incomplete Data Imputation
Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada · Department of Statistical and Actuarial Sciences, Department of Computer Science, University of Western Ontario, Ontario, Canada · Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA +1
DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
Aug 20, 2026Alexander Marusov, Dmitry Anikin, Alexey ZaytsevVariational Autoencoders Time Series Forecasting
Applied AI Institute Moscow, Russia
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Aug 20, 2026Alexander Marusov, Dmitry Anikin, Petr Sokerin +3Variational Autoencoders Multivariate Time Series Forecasting
Applied AI Institute Moscow, Russia
Dual-Primal Graph VAEs for Noisy Label Aggregation
Aug 11, 2026Patrick Stinson, Nikolaus KriegeskorteVariational Autoencoders Graph Neural Networks
Zuckerman Institute Columbia University
$β$ -VAEs as Effective Theories: Tolerance-Dependent Dimension
Aug 11, 2026Johannes HirnVariational Autoencoders Intrinsic Dimensionality
Image Processing Laboratory (IPL), Universitat de València, Paterna, València 46980, Spain
ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation
Aug 11, 2026Ge WangVariational Autoencoders Evidential Deep Learning
Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations
Aug 7, 2026Ioannis Ziogas, Ensieh Khazaei, Bilal Taha +4Variational Autoencoders Human Activity Recognition
Department of Biomedical Engineering and Biotechnology, Khalifa University, 127788 Abu Dhabi, UAE · Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada · MIT Media Lab +2
KVAE: Family of Tokenizers for Multimodal Generative Models
Aug 6, 2026Andrey Shutkin, Denis Parkhomenko, Ivan Kirillov +11Variational Autoencoders Audio Token Compression
Kandinsky Lab
EmbodiedVAE: Disentangled Video VAE for Efficient and Controllable Embodied Manipulation
Aug 4, 2026Jiayi Luo, Hanxin Zhu, Chen Gao +5Variational Autoencoders Disentangled Representation Learning
Beihang University · Zhongguancun Academy · University of Science and Technology of China +3
Latent-Frequency Validity: Fast Spectral Editing with Screened Video-VAE Transfer Operators
Aug 4, 2026Bowen Xue, Jiafeng Xiong, Xin QuanVariational Autoencoders Autoencoders
University of Manchester, United Kingdom · Idiap Research Institute, Switzerland
Beyond Global Latents: Chunk-Based Sparse Grid VAE for Scalable 3D Modeling
Aug 3, 2026Kaiyi Zhang, Zhihao Liang, Haolin Liu +8Variational Autoencoders Autoencoders
Hong Kong University of Science and Technology · Tencent Hunyuan · The Chinese University of Hong Kong

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

MeshCarve: Artisan Mesh Generation with Flow Matching in Compact Latent Spaces

Xiyu Wang, Ruocheng Wu, Yufei Wang, Zhihao Li+2

Nanyang Technological University · SparAI Inc. · University of Texas at Austin

3D Mesh Generation Flow-Based Generative Modeling Flow Matching Variational Autoencoders

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Prior artisan mesh generation works largely predict face tokens autoregressively, which makes inference slow. Recent methods instead flow match continuous latents built by Variational AutoEncoders (VAEs), but reconstruction quality drops significantly when geometry and topology are jointly encoded, and further when the latent space is compressed. We present MeshCarve, a flow matching method that generates entirely in compact latent spaces, generating vertex positions and edge connections separately and sidestepping the difficulty of a joint compact latent. To shorten the token sequence, we propose a hierarchical sparse transformer backbone, instantiated as VertexVAE and EdgeVAE. Instead of encoding fields over the surface voxels, both VAEs anchor on discrete vertices in their latent spaces, which drastically reduces the token sequence length, and our spatial-aware compression shortens it further without costing reconstruction. VertexVAE directly encodes vertex occupancy. For connectivity, we propose vertex-link encoding, which turns arbitrary connectivity between vertices into fixed-length continuous per-vertex embeddings and recovers complex artistic topology faithfully. MeshCarve combines these VAEs with an anchor generator and flow matches on the shortened token sequences. It shows advantages over state-of-the-art autoregressive and flow matching methods on Objaverse and generalizes to Toys4K. To the best of our knowledge, it is among the first artisan mesh generation methods whose every generative stage runs in a spatially compressed latent, with a token sequence only a fraction of the most compressed previous autoregressive and flow matching works.

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