Recent diffusion-based methods for material transfer rely on image fine-tuning or complex architectures with assistive networks, but face challenges including text dependency, extra computational costs, and feature misalignment. To address these limitations, we propose MaTe, a streamlined diffusion framework that eliminates textual guidance and reference networks. MaTe integrates input images at the token level, enabling unified processing via multi-modal attention in a shared latent space. This design removes the need for additional adapters, ControlNet, inversion sampling, or model fine-tuning. Extensive experiments demonstrate that MaTe achieves high-quality material generation under a zero-shot, training-free paradigm. It outperforms state-of-the-art methods in both visual quality and efficiency while preserving precise detail alignment, significantly simplifying inference prerequisites.
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Diffusion models have gained prominence as state-of-the-art techniques for synthesizing images and videos, particularly due to their ability to scale effectively with large datasets. Recent studies have uncovered that these extensive datasets often contain mistakes from manual labeling processes. However, the extent to which such errors compromise the generative capabilities and controllability of diffusion models is not well studied. This paper introduces Score-based Discriminator Correction (SBDC), a guidance technique for aligning noisy pre-trained conditional diffusion models. The guidance is built on discriminator training using adversarial loss, drawing on prior noise detection techniques to assess the authenticity of each sample.We further show that limiting the usage of our guidance to the early phase of the generation process leads to better performance.Our method is computationally efficient, only marginally increases inference time, and does not require retraining diffusion models.Experiments on different noise settings demonstrate the superiority of our method over previous state-of-the-art methods.
Text-to-image diffusion models have seen significant development recently due to increasing availability of paired 2D data. Although a similar trend is emerging in 3D generation, the limited availability of high-quality 3D data has resulted in less competitive 3D diffusion models compared to their 2D counterparts. In this work, we show how 2D diffusion models, originally trained for text-to-image generation, can be repurposed for 3D object generation. We introduce Gaussian Atlas, a representation of 3D Gaussians with dense 2D grids, which enables the fine-tuning of 2D diffusion models for generating 3D Gaussians. Our approach shows a successful transfer learning from a pretrained 2D diffusion model to 2D manifold flattend from 3D structures. To facilitate model training, a large-scale dataset, Gaussian Atlas, is compiled to comprise 205K high-quality 3D Gaussian fittings of a diverse array of 3D objects. Our experiment results indicate that text-to-image diffusion models can also serve as 3D content generators.
We present a simple and scalable text and image conditioned video generation method. Our approach, named STIV, integrates a variable number of image conditions into a Diffusion Transformer (DiT) through frame replacement. This design enables STIV to perform both text-to-video (T2V) and text-image-to-video (TI2V) tasks simultaneously, as well as long video generation through autoregressive rollouts.Additionally, STIV can be easily extended to various applications, such as video prediction, frame interpolation, and multi-view generation, etc.With comprehensive ablation studies on T2I, T2V, TI2V, and long video generation, STIV demonstrate strong performance, despite its simple design. An 8.7B model with (512^2) resolution achieves 83.1 on VBench T2V, surpassing both leading open and closed-source models like CogVideoX-5B, Pika, Kling, and Gen-3. The same-sized model also achieves a state-of-the-art result of 90.1 on VBench I2V task at (512^2) resolution. Combine all of these, we finally scale up our model to 540p with over 200 frames. By providing a transparent recipe for building cutting-edge video generation models, we aim to empower future research and accelerate progress for video generation.
Current training-free text-driven image translation primarily uses diffusion features (convolution and attention) of pre-trained model as guidance to preserve the style/structure of source image in translated image. However, the coarse guidance at feature level struggles with style (e.g., visual patterns) and structure (e.g., edges) alignment with the source. Based on the observation that the low-/high-frequency components retain style/structure information of image, in this work, we propose training-free Frequency-Guided Diffusion (FGD), which tailors low-/high-frequency guidance for style- and structure-guided translation, respectively. For low-frequency guidance (style-guided), we substitute the low-frequency components of diffusion latents from sampling process with those from inversion of source and normalize the obtained latent with composited spectrum to enforce color alignment. For high-frequency guidance (structure-guided), we propose high-frequency alignment and high-frequency injection that compensate each other. High-frequency alignment preserves edges and contour by adjusting the predicted noise with guidance function that aligns high-frequency image regions between sampling and source image. High-frequency injection facilitates layout preservation by injecting high-frequency components of diffusion convolution features (from inversion) to sampling process. Qualitative and quantitative results verify the superiority of our method on style- and structure-guided translation tasks.
One can hardly model self-contact of human poses without considering underlying body shapes. For example, the pose of rubbing a belly for a person with a low BMI leads to penetration of the hand into the belly for a person with a high BMI. Despite its relevance, existing self-contact datasets lack the variety of self-contact poses and precise body shapes, limiting conclusive analysis between self-contact poses and shapes. To address this, we begin by introducing the first extensive self-contact dataset with precise body shape registration, Goliath-SC, consisting of 383K self-contact poses across 130 subjects. Using this dataset, we propose generative modeling of self-contact prior conditioned by body shape parameters, based on a body-part-wise latent diffusion with self-attention. We further incorporate this prior into single-view human pose estimation while refining estimated poses to be in contact. Our experiments suggest that shape conditioning is vital to the successful modeling of self-contact pose distribution, hence improving single-view pose estimation in self-contact.
In the domain of text-to-image generation, diffusion models have emerged as powerful tools. Recently, studies on visual prompting, where images are used as prompts, have enabled more precise control over style and content. However, existing methods often suffer from content leakage, where undesired elements of the visual style prompt are transferred along with the intended style. To address this issue, we 1) extend classifier-free guidance (CFG) to utilize swapping self-attention and propose 2) negative visual query guidance (NVQG) to reduce the transfer of unwanted contents. NVQG employs negative score by intentionally simulating content leakage scenarios that swap queries instead of key and values of self-attention layers from visual style prompts. This simple yet effective method significantly reduces content leakage. Furthermore, we provide careful solutions for using a real image as visual style prompts.Through extensive evaluation across various styles and text prompts, our method demonstrates superiority over existing approaches, reflecting the style of the references, and ensuring that resulting images match the text prompts.
We present a universal prior model for 3D head avatars with explicit hair compositionality. Existing approaches to build generalizable priors for 3D head avatars often adopt a holistic modeling approach, treating the face and hair as an inseparable entity. This overlooks the inherent compositionality of the human head, making it difficult for the model to naturally disentangle face and hair representations, especially when the dataset is limited. Furthermore, such holistic models struggle to support applications like 3D face and hairstyle swapping in a flexible and controllable manner. To address these challenges, we introduce a prior model that explicitly accounts for the compositionality of face and hair, learning their latent spaces separately. A key enabler of this approach is our synthetic hairless data creation pipeline, which removes hair from studio-captured datasets using estimated hairless geometry and texture derived from a diffusion prior. By leveraging a paired dataset of original and synthetic hairless captures, we train disentangled prior models for face and hair, incorporating compositionality as an inductive bias to facilitate effective separation. Our model's inherent compositionality enables seamless transfer of face and hair components between avatars while preserving identity. Additionally, we demonstrate that our model can be fine-tuned in a data-efficient manner using monocular captures to create high-fidelity, hair-compositional 3D head avatars for unseen subjects. These capabilities highlight the practical applicability of our approach in real-world scenarios, paving the way for flexible and expressive 3D avatar generation.
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer
PDF ↗We introduce DC-AR, a novel masked autoregressive (AR) text-to-image generation framework that delivers superior image generation quality with exceptional computational efficiency. Due to the tokenizers' limitations, prior masked AR models have lagged behind diffusion models in terms of quality or efficiency. We overcome this limitation by introducing DC-HT-- a deep compression hybrid tokenizer for AR models that achieves a 32xspatial compression ratio while maintaining high reconstruction fidelity and cross-resolution generalization ability. Building upon DC-HT, we extend MaskGIT and create a new hybrid masked autoregressive image generation framework that first produces the structural elements through discrete tokens and then applies refinements via residual tokens.DC-AR achieves state-of-the-art results with a gFID of 5.49 on MJHQ-30K and an overall score of 0.69 on GenEval, while offering 1.5-7.9xhigher throughput and 2.0-3.5xlower latency compared to prior leading diffusion and masked autoregressive models. We will release the code and pre-trained models upon publication.
Different from color correction and transfer, color grading involves adjusting colors for artistic or storytelling purposes in a video, which is used to establish a specific look or mood. However, due to the complexity of the process and the need for specialized editing skills, video color grading remains primarily the domain of professional colorists. In this paper, we present a reference based video color grading framework. Our key idea is explicitly generating a look-up table (LUT) for color attribute alignment between reference scenes and input video via a diffusion model. As a training objective, we enforce that high-level features of the reference scenes like look, mood, and emotion should be similar to that of the input video. Our LUT-based approach allows for color grading without any loss of structural details in the whole video frames as well as achieving fast inference. We further build a pipeline to incorporate a user-preference via text prompts for low-level feature enhancement such as contrast and brightness, etc. Experimental results, including extensive user studies, demonstrate the effectiveness of our approach for video color grading.
GenFlowRL: Shaping Rewards with Generative Object-Centric Flow in Visual Reinforcement Learning
PDF ↗Recent advances have shown that video generation models can enhance robot learning by deriving effective robot actions through inverse dynamics. However, these methods heavily depend on the quality of generated data and struggle with fine-grained manipulation due to the lack of environment feedback. While video-based reinforcement learning improves policy robustness, it remains constrained by the uncertainty of video generation and the challenges of collecting large-scale robot datasets for training diffusion models. To address these limitations, we propose GENFLOWRL, which derives shaped rewards from generated flow trained from diverse cross-embodiment datasets. This enables learning generalizable and robust policies from diverse demonstrations using low-dimensional, object-centric features. Experiments on 10 manipulation tasks, both in simulation and real-world cross-embodiment evaluations, demonstrate that GENFLOWRL effectively leverages manipulation features extracted from generated object-centric flow, consistently achieving superior performance across diverse and challenging scenarios. Our Project Page: https://colinyu1.github.io/genflowrl/.
Few-shot font generation (FFG) aims to create new font images by imitating the style from a limited set of reference images, while maintaining the content from the source images. Although this task has achieved significant progress, most existing methods still suffer from the incorrect generation of complicated character structure and detailed font style. To address the above issues, in this paper, we regard font generation as a font transfer process from the source font to the target font, and construct a video generation framework to model this process. Moreover, a test-time condition alignment mechanism is further developed to enhance the consistency between the generated samples and the provided condition samples. Specifically, we first construct a diffusion-based image-to-image font generation framework for the few-shot font generation task. This framework is expanded into an image-to-video font generation framework by integrating temporal components and frame-index information, enabling the production of high-quality font videos that transition from the source font to the target font. Based on this framework, we develop a noise inversion mechanism in the generative process to perform content and style alignment between the generated samples and the provided condition samples, enhancing style consistency and structural accuracy. The experimental results show that our model achieves superior performance on FFG tasks, demonstrating the effectiveness of our method. We will release our code after publication.
Neural representations of 3D data have been widely adopted across various applications, particularly in recent work leveraging coordinate-based networks to model scalar or vector fields. However, these approaches face inherent challenges, such as handling thin structures and non-watertight geometries, which limit their flexibility and accuracy. In contrast, we propose a novel geometric data representation that models geometry as distributions-a powerful representation that makes no assumptions about surface genus, connectivity, or boundary conditions. Our approach uses diffusion models with a novel network architecture to learn surface point distributions, capturing fine-grained geometric details. We evaluate our representation qualitatively and quantitatively across various object types, demonstrating its effectiveness in achieving high geometric fidelity. Additionally, we explore applications using our representation, such as textured mesh representation, neural surface compression, dynamic object modeling, and rendering, highlighting its potential to advance 3D geometric learning.
Guiding Diffusion-Based Articulated Object Generation by Partial Point Cloud Alignment and Physical Plausibility Constraints
PDF ↗Articulated objects are an important type of interactable objects in everyday environments. In this paper, we propose PhysNAP, a novel diffusion model-based approach for generating articulated objects that aligns them with partial point clouds and improves their physical plausibility. The model represents part shapes by signed distance functions (SDFs). We guide the reverse diffusion process using a point cloud alignment loss computed using the predicted SDFs. Additionally, we impose non-penetration and mobility constraints based on the part SDFs for guiding the model to generate more physically plausible objects. We also make our diffusion approach category-aware to further improve point cloud alignment if category information is available. We evaluate the generative ability and constraint consistency of samples generated with PhysNAP using the PartNet-Mobility dataset. We also compare it with an unguided baseline diffusion model and demonstrate that PhysNAP can improve constraint consistency and provides a tradeoff with generative ability.
Current end-to-end multi-modal models utilize different encoders and decoders to process input and output information. This separation hinders the joint representation learning of various modalities. To unify multi-modal processing, we propose a novel architecture called MDM (Multi-modal Diffusion Mamba). MDM utilizes a Mamba-based multi-step selection diffusion model to progressively generate and refine modality-specific information through a unified variational autoencoder for both encoding and decoding. This innovative approach allows MDM to achieve superior performance when processing high-dimensional data, particularly in generating high-resolution images and extended text sequences simultaneously. Our evaluations in areas such as image generation, image captioning, visual question answering, text comprehension, and reasoning tasks demonstrate that MDM significantly outperforms existing end-to-end models (MonoFormer, LlamaGen, and Chameleon etc.) and competes effectively with SOTA models like GPT-4V, Gemini Pro, and Mistral. Our results validate MDM's effectiveness in unifying multi-modal processes while maintaining computational efficiency, establishing a new direction for end-to-end multi-modal architectures.
GSV3D: Gaussian Splatting-based Geometric Distillation with Stable Video Diffusion for Single-Image 3D Object Generation
PDF ↗Image-based 3D generation has vast applications in robotics and gaming, where high-quality, diverse outputs and consistent 3D representations are crucial. However, existing methods have limitations: 3D diffusion models are limited by dataset scarcity and the absence of strong pre-trained priors, while 2D diffusion-based approaches struggle with geometric consistency. We propose a method that leverages 2D diffusion models' implicit 3D reasoning ability while ensuring 3D consistency via Gaussian-splatting-based geometric distillation. Specifically, the proposed Gaussian Splatting Decoder enforces 3D consistency by transforming SV3D latent outputs into an explicit 3D representation. Unlike SV3D, which only relies on implicit 2D representations for video generation, Gaussian Splatting explicitly encodes spatial and appearance attributes, enabling multi-view consistency through geometric constraints. These constraints correct view inconsistencies, ensuring robust geometric consistency. As a result, our approach simultaneously generates high-quality, multi-view-consistent images and accurate 3D models, providing a scalable solution for single-image-based 3D generation and bridging the gap between 2D Diffusion diversity and 3D structural coherence. Experimental results demonstrate state-of-the-art multi-view consistency and strong generalization across diverse datasets. Our code is available at https://github.com/MOMOYATW/GSV3D.
We propose MotionAgent, enabling fine-grained motion control for text-guided image-to-video generation. The key technique is the motion field agent that converts motion information in text prompts into explicit motion fields, providing flexible and precise motion guidance. Specifically, the agent extracts the object movement and camera motion described in the text, and converts them into object trajectories and camera extrinsics, respectively. An analytical optical flow composition module integrates these motion representations in 3D space and projects them into a unified optical flow. An optical flow adapter takes the flow to control the base image-to-video diffusion model for generating fine-grained controlled videos. After that, an optional rethinking step can be adopted to ensure the generated video is aligned well with motion information in the prompt. The significant improvement in the Video-Text Camera Motion metrics on VBench indicates that our method achieves precise control over camera motion. We further construct a subset of VBench to evaluate the alignment of motion information in the text and the generated video, outperforming other advanced models on motion generation accuracy.
The recognition of Ancient Egyptian hieroglyphs poses persistent challenges due to limited annotated data and wide stylistic variation. We introduce the Multisource Egyptian Hieroglyphs (MEH) dataset, a new benchmark that captures a diverse range of writing styles with detailed clause-level and OCR annotations derived from expert-curated sources. To address the scarcity of training data, we propose a method for generating synthetic hieroglyphic sequences by combining real linguistic material, hierarchical layout modeling, and diffusion-based rendering. We evaluate both multi-stage and end-to-end OCR systems on MEH, studying the effects of synthetic pretraining, model scale, and cross-style transfer. To enable future expansion, we release pyThoth, an annotation tool with built-in model assistance for streamlined human-in-the-loop labeling. We believe that these contributions lay the groundwork for building an AI-powered research assistant for Egyptology.
MR-FIQA: Face Image Quality Assessment with Multi-Reference Representations from Synthetic Data Generation
PDF ↗Recent advancements in Face Image Quality Assessment (FIQA) models trained on real large-scale face datasets are pivotal in guaranteeing precise face recognition in unrestricted scenarios. Regrettably, privacy concerns lead to the discontinuation of real datasets, underscoring the pressing need for a tailored synthetic dataset dedicated to the FIQA task. However, creating satisfactory synthetic datasets for FIQA is challenging. It requires not only controlling the intra-class degradation of different quality factors (e.g., pose, blur, occlusion) for the pseudo-identity generation but also designing an optimized quality characterization method for quality annotations. This paper undertakes the pioneering initiative to establish a Synthetic dataset for FIQA (SynFIQA) based on a hypothesis: accurate quality labelling can be achieved through the utilization of quality priors across the diverse domains involved in quality-controllable generation. To validate this, we tailor the generation of reference and degraded samples by aligning pseudo-identity image features in stable diffusion latent space, editing 3D facial parameters, and customizing dual text prompts and post-processing. Furthermore, we propose a novel quality characterization method that thoroughly examines the relationship of Multiple Reference representations among recognition embedding, spatial, and visual-language domains to acquire annotations essential for fitting FIQA models (MR-FIQA). Extensive experiments confirm the validity of our hypothesis and demonstrate the advantages of our SynFIQA data and MR-FIQA method.
We introduce Bokehdiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous methods are bounded by the accuracy of depth estimation, generating artifacts in depth discontinuities. Our method employs a physics-inspired self-attention module that aligns with the image formation process, incorporating depth-dependent circle of confusion constraint and self-occlusion effects. We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity. To address the lack of scalable paired data, we propose to synthesize photorealistic foregrounds with transparency with diffusion models, balancing authenticity and scene diversity.