New research explores diffusion model advancements for image and video generation · 9 sources tracked
ByPulseAugur Editorial·[9 sources]·
Multiple research papers released on arXiv explore advancements in diffusion models for image and video generation. These studies introduce novel techniques such as landmark-constrained acceleration for vector diffusion maps, tensor-based functionals for image enhancement, and channel representations for non-linear diffusion filtering. Other papers focus on improving the efficiency and generalization of diffusion models, including methods for fast sampling, elastic token compression, and waypoint diffusion transformers for pixel-space generation. Additionally, research addresses test-time scaling for video diffusion models through candidate recycling and discrete diffusion bridges for spatiotemporally aligned image translation.
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These papers introduce novel techniques and optimizations for diffusion models, potentially improving image and video generation quality and efficiency.
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Multiple arXiv papers detailing novel research in diffusion models.
arXiv:2603.21247v2 Announce Type: replace-cross Abstract: We propose a landmark-constrained algorithm, LA-VDM (Landmark Accelerated Vector Diffusion Maps), to accelerate the Vector Diffusion Maps (VDM) framework built upon the Graph Connection Laplacian (GCL), which captures pair…
arXiv cs.CV
TIER_1English(EN)·Freddie {\AA}str\"om, Michael Felsberg, George Baravdish·
arXiv:2608.29164v1 Announce Type: new Abstract: In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application dependent and contextual information using first principl…
arXiv cs.CV
TIER_1English(EN)·Christian Heinemann, Freddie {\AA}str\"om, George Baravdish, Kai Krajsek, Michael Felsberg, Hanno Scharr·
arXiv:2608.29227v1 Announce Type: new Abstract: In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representa…
arXiv:2608.29233v1 Announce Type: new Abstract: We present the winning solution to the ACM Multimedia 2026 Grand Challenge on Single-Image Guided Multi-Angle Image Synthesis. It ranks first among 293 registered teams; 56 teams obtained at least one scored submission on the public…
arXiv cs.CV
TIER_1English(EN)·Eduard Zamfir, Christian Reisswig, Zongwei Wu, Yongqin Xian, Radu Timofte·
arXiv:2608.29281v1 Announce Type: new Abstract: Natural images concentrate their detail in a small fraction of the frame, yet diffusion models spend a full token on every patch, in every layer and at every timestep. The waste is largest in pixel-space models, with no autoencoder …
arXiv:2608.29322v1 Announce Type: new Abstract: Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or costly large-scale infrastructure. Test-time scaling (TTS) offers a training-free …
arXiv:2608.29997v1 Announce Type: new Abstract: We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete diffusion in image translation and generation. By corrupting data into a pure mask s…
arXiv cs.CV
TIER_1English(EN)·Zhenyu Zhou, Defang Chen, Siwei Lyu, Chun Chen, Can Wang·
arXiv:2603.00763v2 Announce Type: replace Abstract: Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed i…
arXiv:2603.15132v3 Announce Type: replace Abstract: While recent Flow Matching models avoid the reconstruction bottlenecks of latent autoencoders by operating directly in pixel space, the raw pixel manifold provides little explicit semantic organization, making target-specific tr…
