Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2608.27885v1 Announce Type: new Abstract: Multimodality translation (e.g., text-to-image) is a core generative AI task. However, existing approaches (1) follow generative paths that do not dire...
arXiv:2607. 26735v1 Announce Type: cross Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering.
The paper introduces DSG‑GI, a method that adapts pretrained latent video diffusion models for generative inbetweening by using Destination‑Conditioned Adaptation and Semantic‑Geometric Alignment. Destination‑Conditioned Adaptation treats the target frame as the sequence’s endpoint, providing progressive guidance along the generated trajectory. Semantic‑Geometric Alignment aligns token‑wise spatio‑temporal geometry of backbone features with a frozen video encoder and enforces a flow‑matching latent flow constraint near the data endpoint, thereby improving temporal stability, target‑frame faithfulness, and perceptual quality on large‑motion benchmarks.
AlignMorph is a tuning‑free diffusion framework for image morphing that separates geometric alignment from generative denoising. It uses Global Semantic Transport—entropic optimal transport and reliability‑aware latent warping—to achieve diffusion‑compatible semantic alignment, and Coordinate‑Aligned Generation—symmetric bi‑phase attention handoff—to preserve spatial coordinates during denoising. The method eliminates ghosting and delivers superior structural coherence and temporal smoothness on morphing benchmarks without any per‑pair optimization.
arXiv:2607. 16294v1 Announce Type: cross Abstract: Paired image-to-image translation underpins a wide range of computer vision tasks, including image editing, sensor translation, and domain adaptation.
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 m...