ARXIV:2605.00644 · MULTIMODAL GENERATIVE MODELS · SUBMITTED 04 MAY · 20:24 UTC · FRESHNESS STALE

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Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision

Jiali Cui · Zhiqiang Lao · Heather Yu · arXiv

This work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements.

Ship in 2-4 weeks›Score4.0Evidence unverified

Opportunity summary

Pain This work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements.

Evidence 0 refs | 4 sources | 50% coverage

Blocker Evidence unverified

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PROBLEM

This work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements. However, learning multimodal EBM by maximum likelihood requires Markov Chain…

METHOD

Full abstract

Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM by maximum likelihood requires Markov Chain Monte Carlo (MCMC) sampling in the joint data space, where noise-initialized Langevin dynamics often mixes poorly and fails to discover coherent inter-modal relationships. Multimodal VAEs have made progress in capturing such inter-modal dependencies by introducing a shared latent generator and a joint inference model. However, both the shared latent generator and joint inference model are parameterized as unimodal Gaussian (or Laplace), which severely limits their ability to approximate the complex structure induced by multimodal data. In this work, we study the learning problem of the multimodal EBM, shared latent generator, and joint inference model. We present a learning framework that effectively interweaves their MLE updates with corresponding MCMC refinements in both the data and latent spaces. Specifically, the generator is learned to produce coherent multimodal samples that serve as strong initial states for EBM sampling, while the inference model is learned to provide informative latent initializations for generator posterior sampling. Together, these two models serve as complementary models that enable effective EBM sampling and learning, yielding realistic and coherent multimodal EBM samples. Extensive experiments demonstrate superior performance for multimodal synthesis quality and coherence compared to various baselines. We conduct various analyses and ablation studies to validate the effectiveness and scalability of the proposed multimodal framework.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. Together, these two models serve as complementary models that enable effective EBM sampling and learning, yielding realistic and coherent multimodal EBM samples. A public…

WHY NOW

Multimodal Generative Models moved forward this cycle; last verified May 2026. Public score 4.0/10. Implementation evidence is present through a linked repository.

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Opportunity summary

Score4.0

PainThis work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements.

Evidence0 refs | 4 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

This work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

This work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements.

Segment

Multimodal Generative Models

Adoption evidence

Public code linked for build inspection

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

This work presents a framework to learn multimodal energy-based models and VAEs together, improving coherence and realism in generated samples through interleaved MCMC refinements.

Segment

Multimodal Generative Models

Adoption evidence

Public code linked for build inspection

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision

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