MatMart: Material Reconstruction of 3D Objects via Diffusion

A new framework called FeRA has been introduced to enhance the adaptation of diffusion models for generative tasks. By focusing on the frequency energy mechanism during denoising, FeRA aligns parameter updates with the intrinsic energy progression of diffusion, comprising components like a frequency energy indicator and a soft frequency router.

A new zero-shot video deraining method has been introduced, leveraging a pretrained text-to-video diffusion model to effectively remove rain from complex dynamic scenes without the need for synthetic data or model fine-tuning. This approach marks a significant advancement in video deraining technology, addressing limitations of existing methods that rely on paired datasets or static camera setups.

A recent study has introduced a comprehensive evaluation framework for dataset watermarking in fine-tuning diffusion models, addressing the need for traceability in customized image generation. This framework assesses methods based on Universality, Transmissibility, and Robustness, revealing vulnerabilities in existing watermarking techniques under real-world scenarios.

A new approach to image compression, known as Hybrid-Diffusion Image Compression (HDCompression), has been introduced to tackle the challenges of achieving high fidelity and perceptual quality at ultra-low bitrates. This dual-stream framework combines generative vector-quantized modeling, diffusion models, and conventional learned image compression techniques to enhance image quality while minimizing artifacts caused by heavy quantization.

SCALEX has been introduced as a framework for scalable and automated exploration of latent spaces in diffusion models, addressing the limitations of existing methods that often rely on predefined categories or manual interpretation. This framework utilizes natural language prompts to extract semantically meaningful directions, enabling zero-shot interpretation and systematic comparisons across various concepts.