mlsanityData to 14 Oct 2026

Future Video Generation Better Aligns with the Human Visual Cortex than Observed Video

arXiv 2609.38819 ↗First posted 30 Sep 2026cs.CV cs.AI q-bio.NCcited by 0

On the map: Diffusion models → Video generation

Abstract (arXiv)

Studying the alignment between the internal representations of vision models and the responses of the visual cortex to the same observed visual stimuli has enabled us to better understand human visual processing. However, studies so far have largely overlooked the fact that the human brain not only processes observed visual stimuli, but also predicts upcoming stimuli based on what has been observed. Accordingly, we hypothesize that internal representations for generating future video frames are better aligned with the predictive nature of human visual processing than representations of the observed video itself. To this end, we compare the alignment between human video-watching fMRI responses in the visual cortex and the internal representations from two types of video diffusion models, an autoregressive (AR) model and its non-AR base model. We first conduct a within-model analysis of the AR video diffusion model and show that the representations for future video generation align better with the visual cortex than the representations of the observed video. We then compare the internal representations of the AR model with those of its non-AR base model and again show that the representations for future video generation align better with the visual cortex than the representations for observed video reconstruction by the base model. Specifically, the alignment of observed video reconstruction is concentrated in lower-order visual cortex, whereas that of future video generation is concentrated in higher-order visual cortex. Finally, we show in a human behavioral experiment that humans prefer videos generated by amplifying the contributions of individual layers that align better with the visual cortex.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 22 of its 57 references that are in the map. First posted on arXiv 30 Sep 2026.

Builds on

22 of 57 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Gen4U: Unifying Video Generation and Understanding via Diffusion7 Jul
  2. 2026The Invisible Hand of Physics: When Video Diffusion Models Know More Than They Show3 Jun
  3. 2026Video-Mirai: Autoregressive Video Diffusion Models Need Foresight2 Jun
  4. 2025Denoise to Track: Harnessing Video Diffusion Priors for Robust Correspondence4 Dec
  5. 2025Emergent Temporal Correspondences from Video Diffusion Transformers20 Jun
  6. 2025Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion9 Jun
  7. 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Models10 Dec

Other references · 15

  1. 2022Scalable Diffusion Models with Transformerscited by 6,054
  2. 2022Flow Matching for Generative Modelingcited by 5,702
  3. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
  4. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 703
  5. 2023TokenFlow: Consistent Diffusion Features for Consistent Video Editingcited by 442
  6. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 306
  7. 2023Denoising Diffusion Autoencoders are Unified Self-supervised Learnerscited by 130
  8. 2024Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidancecited by 113
  9. 2014Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathwaycited by 94
  10. 2026Helios: Real Real-Time Long Video Generation Modelcited by 65
  11. 2023Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Latercited by 55
  12. 2018Cognitive computational neurosciencecited by 43
3 more
  1. 2024Spatiotemporal Skip Guidance for Enhanced Video Diffusion Samplingcited by 34
  2. 2026Only Brains Align with Brains: Cross-Region Alignment Patterns Expose Limits of Normative Modelscited by 4
  3. 2025Diffusion-Based Action Recognition Generalizes to Untrained Domainscited by 2

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  1. 2025Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstruction
  2. 2025From Image to Video: An Empirical Study of Diffusion Representations
  3. 2026The Pictorial Cortex: Zero-Shot Cross-Subject fMRI-to-Image Reconstruction via Compositional Latent Modeling
  4. 2023Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity
  5. 2023Reconstructing seen images from human brain activity via guided stochastic search
  6. 2026Do Models See in Line with Human Vision? Probing the Correspondence Between LVLM Representations and EEG Signals
  7. 2024NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties
  8. 2023Decoding Realistic Images from Brain Activity with Contrastive Self-supervision and Latent Diffusion