In this paper, we present LaVieID, a novel local a utoregressive vi deo diffusion framework designed to tackle the challenging id entity-preserving text-to-video task. The key idea of LaVieID is to mitigate the loss of identity information inherent in the stochastic global generation process of diffusion transformers (DiTs) from both spatial and temporal perspectives. Specifically, unlike the global and unstructured modeling of facial latent states in existing DiTs, LaVieID introduces a local router to explicitly represent latent states by weighted combinations of fine-grained local facial structures. This alleviates undesirable feature interference and encourages DiTs to capture distinctive facial characteristics. Furthermore, a temporal autoregressive module is integrated into LaVieID to refine denoised latent tokens before video decoding. This module divides latent tokens temporally into chunks, exploiting their long-range temporal dependencies to predict biases for rectifying tokens, thereby significantly enhancing inter-frame identity consistency. Consequently, LaVieID can generate high-fidelity personalized videos and achieve state-of-the-art performance. Our code and models are available at https://github.com/ssugarwh/LaVieID.
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Recent advances in text-to-image diffusion models have demonstrated remarkable capabilities in generating high-quality visual content with style and feature controlled. A fundamental challenge remains in simultaneously maintaining three critical properties of generated image sequences: (1) fine-grained style control, (2) strict image-prompt alignment, and (3) cross-image content coherence. To overcome the challenge, we leverage AnyStyleDiffusion to overcome the challenge. Specifically, we interpret any artistic style required by users on generated image as a feature in models' weight space. Interpolation between weight space obtains models expressing middle styles with linear transition. Hyper-receptive Motion Layers is proposed to align outputs of diverse weight spaces, operating as adaptive style modulators. These HRMLs are separated from interpolated diffusion models, leveraging zero-shot compatibility with existing model checkpoints. By employing Homogeneous Stable Diffusion, direct interpolation on weight space is avoided to improve synthesis efficiency. Comprehensive evaluations across personalized models demonstrate our method's superiority in generating content-coherent sequences with dynamic style transformations. Code will be released at https://github.com/shermandozer/AnyStyleDiffusion.git.
Recent advances in image editing systems reveal critical limitations in handling complex real-world scenarios requiring multimodal condition controls. While text instructions enable broad semantic guidance, visual examples provide precise visual reference in specific scenarios, existing unimodal approaches fail to synergize these complementary modalities effectively. We propose EditMaster, a unified framework that integrates text and visual controls through multimodal instruction learning, enabling precise image manipulation with bidirectional consistency. Our framework introduces three core innovations: A multimodal large language model enhanced with extended visual tokens replaces CLIP text encoders, generating pre-edited visual guidance that aligns textual commands with visual examples to guide diffusion model toward high-quality outputs; The Mask-Based Decoupled Residual Exemplar-Attention module preserves unedited regions through spatial masking while integrating visual details via residual pathways; A systematic data construction method converts unimodal editing datasets into a task-specific multimodal dataset, eliminating the need for de novo data construction. Experiments show that our approach outperforms unimodal baselines and excels in complex multimodal instruction editing, setting a new benchmark for this field.
Recent text-to-image diffusion models have achieved remarkable success in generating high-quality images. However, their exclusive reliance on textual prompts falls short in precise control of image compositions. In this paper, we propose LoCo, a training-free approach for layout-to-image synthesis that excels in producing high-quality images aligned with both textual prompts and layout instructions. Specifically, LoCo features a novel Localized Attention Constraint, which utilizes the semantic affinity between pixels in self-attention maps to create precise representations of desired objects, thereby ensuring their accurate placement within designated regions. We further introduce a Padding Token Constraint to leverage the semantic information embedded in previously overlooked padding tokens, improving the consistency between object appearance and layout instructions. Our method seamlessly integrates with existing text-to-image and layout-to-image models, improving their spatial control capabilities and addressing semantic failures seen in prior approaches. Extensive experiments demonstrate the superiority of LoCo, outperforming state-of-the-art training-free layout-to-image methods both qualitatively and quantitatively across multiple benchmarks.
With the rapid progress of diffusion-based Text-to-Image Generation (TIG), Text-to-Image Editing (TIE) has become increasingly important for enabling controllable visual content creation. A core challenge in TIE is generating text-guided edits while preserving the spatial structure of the original image. Recent methods attempt to address this by leveraging self-attention maps from diffusion models, as these encode rich spatial information. However, we identify two key limitations: (1) not all self-attention maps contribute meaningfully to spatial structure, and (2) over-reliance on them can suppress desired editing effects. To address this, we propose the Spatial Information Score (SIS), a novel metric that quantifies the spatial structure encoded in each self-attention map. Leveraging SIS, we develop Selective Self-Attention-based Image Manipulation (SSAIM), which selectively utilizes self-attention maps with effective spatial structure (high SIS) to preserve the structural of the original image and reduce excessive reliance on self-attention maps with ineffective spatial structure (low SIS) to enhance editing performance in TIE tasks. Extensive experiments across diverse TIE tasks demonstrate that SSAIM significantly improves both structural fidelity and editing quality.
Trajectory distillation based on consistency models (CMs) provides an effective framework for accelerating diffusion models by reducing inference steps. However, we find that existing CMs degrade style similarity and compromise aesthetic quality in stylization tasks-especially when handling image-to-image or video-to-video transformations that start denoising from partially noised inputs. The core limitation stems from existing methods enforcing initial-step alignment between the probability flow ODE (PF-ODE) trajectories of student models and their imperfect teacher models. This partial alignment strategy inevitably fails to guarantee full trajectory consistency, thereby compromising the overall generation quality. To address this issue, we propose Single Trajectory Distillation (STD), a training framework initiated from partial noise states. To counteract the additional time overhead introduced by STD, we design a trajectory bank that pre-stores intermediate states of the teacher model's PF-ODE trajectories, effectively offsetting the computational cost during student model training. This mechanism ensures STD maintains equivalent training efficiency compared to conventional consistency models. Furthermore, we incorporate an asymmetric adversarial loss to explicitly enhance style consistency and perceptual quality in generated outputs. Extensive experiments on image and video stylization demonstrate that our method surpasses existing acceleration models in terms of style similarity and aesthetic evaluations. Our code and results are available on the project page: https://single-trajectory-distillation.github.io/.
In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a deterministic sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring that the final result is consistent with the input image's structures. We also introduce the Alpha Reliability Propagation, a novel technique designed to maximize the utility of available guidance by selectively enhancing the trimap regions with confident alpha information, thus simplifying the correction task. To train the correction module, we devise specialized loss functions that target the accuracy of the alpha matte's edges and the consistency of its opaque and transparent regions. We evaluate our model across several image matting benchmarks, and the results indicate that DiffusionMat consistently outperforms existing methods.
Image deocclusion (or amodal completion) aims to recover the invisible regions (\ie, shape and appearance) of occluded instances in images. Despite recent advances, the scarcity of high-quality data that balances diversity, plausibility, and fidelity remains a major obstacle. To address this challenge, we identify three critical elements: leveraging in-the-wild image data for diversity, incorporating human expertise for plausibility, and utilizing generative priors for fidelity. We propose SynergyAmodal, a novel framework for co-synthesizing in-the-wild amodal datasets with comprehensive shape and appearance annotations, which integrates these elements through a tripartite data-human-model collaboration. First, we design an occlusion-grounded self-supervised learning algorithm to harness the diversity of in-the-wild image data, fine-tuning an inpainting diffusion model into a partial completion diffusion model. Second, we establish a co-synthesis pipeline to iteratively filter, refine, select, and annotate the initial deocclusion results of the partial completion diffusion model, ensuring plausibility and fidelity through human expert guidance and prior model constraints. This pipeline generates a high-quality paired amodal dataset with extensive category and scale diversity, comprising approximately 16K pairs. Finally, we train a full completion diffusion model on the synthesized dataset, incorporating text prompts as conditioning signals. Extensive experiments demonstrate the effectiveness of our framework in achieving zero-shot generalization and textual controllability. Our code, dataset, and models will be made publicly available at https://github.com/imlixinyang/SynergyAmodal.
Recent advances in diffusion transformer models for motion-guided video generation, such as Tora, have shown significant progress. In this paper, we present Tora2, an enhanced version of Tora, which introduces several design improvements to expand its capabilities in both appearance and motion customization. Specifically, we introduce a decoupled personalization extractor that generates comprehensive personalization embeddings for multiple open-set entities, better preserving fine-grained visual details compared to previous methods. Building on this, we design a gated self-attention mechanism to integrate trajectory, textual description, and visual information for each entity. This innovation significantly reduces misalignment in multimodal conditioning during training. Moreover, we introduce a contrastive loss that jointly optimizes trajectory dynamics and entity consistency through explicit mapping between motion and personalization embeddings. Tora2 is, to our best knowledge, the first method to achieve simultaneous multi-entity customization of appearance and motion for video generation. Experimental results demonstrate that Tora2 achieves competitive performance with state-of-the-art customization methods while providing advanced motion control capabilities, which marks a critical advancement in multi-condition video generation.
Recent large-scale video datasets have facilitated the generation of diverse videos of Video Diffusion Models (VDMs). Nonetheless, some complex actions have still struggled to be generated by those VDMs, leading to a reduction in video generalization. Some researchers attempt to use video editing methods for complex action generation. However, the actions in generated videos are often identical to the reference video, resulting in a lack of diversity. To this end, we first propose Acton In-Context Learning (AICL), a novel approach to generate intricate actions by emulating motions from pre-existing videos in the inference stage using a plug-and-play method. Specifically, the Action Perceiver (AP), is introduced to distill action features from reference videos, which requires training on only a small dataset. Leveraging the knowledge from pre-trained VDMs, Action Integration is introduced for incorporating new action features extracted by AP into VDMs through the additional layers. Extensive experiments demonstrate that AICL is not merely replicating the motion from references, and it significantly improves the generation of realistic actions, even in situations where existing VDMs might directly fail.
Generating human motion in scenes from text aims to synthesize semantically aligned and scene-aware motions. Existing methods have made significant progress by incorporating spatial reasoning and structured generation strategies to connect text descriptions with human-scene interactions. However, they typically rely on simple textual inputs and struggle to comprehend open-ended instructions. There are three key challenges: (1) difficulty in understanding complex instructions due to limited and templated training text annotations; (2) inability to generate natural motions that align with arbitrary trajectories described in text; (3) lack of motion diversity that matches the intended semantics. To address these challenges, we propose PSMo, which consists of two components: the Semantic Planner and the Scene-Aware Motion Generator. The Semantic Planner leverages a Multimodal Large Language Model (MLLM) to parse open-ended instructions, and plans fine-grained motion states aligned with arbitrary trajectories. The scene-aware motion generator adopts the diffusion model with trajectory constraints and a sequential tiling strategy. To enhance motion diversity, we introduce a retrieval-augmented strategy and Scene-Aware Retrieval Attention, which integrates multi-modal features into the generation process. Extensive experiments demonstrate that our method produces high-quality and natural motions under open-ended instructions in scenes.
Diffusion model has been used in indoor scene synthesis and has made significant progress. Current works encode an indoor scene as a top-down view of the room, a list of objects, and their world co-ordinates and orientation. In this paper, we develop a diffusion-based training and synthetic method which incorporates indoor scene ''characteristics''. Firstly, we calculate the relative transformations among objects to capture the local characteristics of the scene. We send this relative transformation into the self-attention layer of the denoising network as ''relative positional encoding''. Secondly, we use room guidance to guide the objects to fit the room's geometry. This improvement uses the room's characteristics to solve the physical collision problem occurring in former diffusion-based works, while preserving plausibilities. Experiments show that our improvements improve the scene variety and quality.
Novel view synthesis (NVS) from a single image is highly ill-posed due to large unobserved regions, especially for views that deviate significantly from the input. While existing methods focus on consistency between the source and generated views, they often fail to maintain coherence and correct view alignment across long-range or looped trajectories. We propose a model that addresses this by decomposing single-view NVS into a 360-degree scene extrapolation followed by novel view interpolation. This design ensures long-term view and scene consistency by conditioning on keyframes extracted and warped from a generated panoramic representation. In the first stage, a panorama diffusion model learns the scene prior from the input perspective image. Perspective keyframes are then sampled and warped from the panorama and used as anchor frames in a pre-trained video diffusion model, which generates novel views through a proposed spatial noise diffusion process. Compared to the prior work, our method produces globally consistent novel views-even in loop-closure scenarios, while enabling flexible camera control. Experiments on diverse scene datasets demonstrate that our approach outperforms existing methods in generating coherent views along user-defined trajectories. Our implementation is available at https://github.com/YiGuYT/LookBeyond.
Autonomous driving has seen remarkable advancements, largely driven by extensive real-world data collection. However, acquiring diverse and corner-case data remains costly and inefficient. Generative models have emerged as a promising solution by synthesizing realistic sensor data. However, existing approaches primarily focus on single-modality generation, leading to inefficiencies and misalignment in multimodal sensor data. To address these challenges, we propose OminiGen, which generates aligned multimodal sensor data in a unified framework. Our approach leverages a shared Bird's Eye View (BEV) space to unify multimodal features and designs a novel generalizable multimodal reconstruction method, UAE, to jointly decode LiDAR and multi-view camera data. UAE achieves multimodal sensor decoding through volume rendering, enabling accurate and flexible reconstruction. Furthermore, we incorporate a Diffusion Transformer (DiT) with a ControlNet branch to enable controllable multimodal sensor generation. Our comprehensive experiments demonstrate that OminiGen achieves desired performances in unified multimodal sensor data generation with multimodal consistency and flexible sensor adjustments.
Current 3D generation methods struggle to balance quality, efficiency, and controllability. This work introduces a collaborative 3D representation framework that leverages a proxy mesh as an intermediate representation. On the one hand, the proxy mesh establishes structural associations with the skeleton, guiding the generation of skeleton bindings that better align with target shape characteristics. On the other hand, it enables adaptive Gaussian sampling in the shape space for efficient rendering. Through the multi-level dependencies and collaboration among skeleton, proxy mesh, and 2DGS, image gradients obtained from diffusion models via the SDS method can synchronously and differentiably update the parameters of Gaussians, mesh shapes, and skeletons. This enables efficient generation of high-quality, editable, and driveable assets (with skeleton binding) under user-specified instructions. The proposed framework demonstrates its efficiency, high fidelity, and precise binding results in 3D rigged asset generation tasks.
Video try-on replaces clothing in videos with target garments. Existing methods struggle to generate high-quality and temporally consistent results when handling complex clothing patterns and diverse body poses. We present 3DV-TON, a novel diffusion-based framework for generating high-fidelity and temporally consistent video try-on results. Our approach employs generated animatable textured 3D meshes as explicit frame-level guidance, alleviating the issue of models over-focusing on appearance fidelity at the expanse of motion coherence. This is achieved by enabling direct reference to consistent garment texture movements throughout video sequences. The proposed method features an adaptive pipeline for generating dynamic 3D guidance: (1) selecting a keyframe for initial 2D image try-on, followed by (2) reconstructing and animating a textured 3D mesh synchronized with original video poses. We further introduce a robust rectangular masking strategy that successfully mitigates artifact propagation caused by leaking clothing information during dynamic human and garment movements. To advance video try-on research, we introduce HR-VVT, a high-resolution benchmark dataset containing 130 videos with diverse clothing types and scenarios. Quantitative and qualitative results demonstrate our superior performance over existing methods.
Computed Tomography (CT) is a widely utilized imaging modality in clinical settings. Using densely acquired rotational X-ray arrays, CT can capture 3D spatial features. However, it is confronted with challenged such as significant time consumption and high radiation exposure. CT reconstruction methods based on sparse-view X-ray images have garnered substantial attention from researchers as they present a means to mitigate costs and risks. In recent years, diffusion models, particularly the Latent Diffusion Model (LDM), have demonstrated promising potential in the domain of 3D CT reconstruction. Nonetheless, due to the substantial differences between the 2D latent representation of X-ray modalities and the 3D latent representation of CT modalities, the vanilla LDM is incapable of achieving effective alignment within the latent space. To address this issue, we propose the Consistent Latent Space Diffusion Model (CLS-DM), which incorporates cross-modal feature contrastive learning to efficiently extract latent 3D information from 2D X-ray images and achieve latent space alignment between modalities. Experimental results indicate that CLS-DM outperforms classical and state-of-the-art generative models in terms of standard voxel-level metrics (PSNR, SSIM) on the LIDC-IDRI and CTSpine1K datasets. This methodology not only aids in enhancing the effectiveness and economic viability of sparse X-ray reconstructed CT but can also be generalized to other cross-modal transformation tasks, such as text-to-image synthesis. We have made our code publicly available at https://anonymous.4open.science/r/CLS-DM-50D6/ to facilitate further research and applications in other domains.
Generating 3D human motion from text descriptions remains challenging due to the diverse and complex nature of human motion. While existing methods excel within the training distribution, they often struggle with out-of-distribution motions, limiting their applicability in real-world scenarios. Existing VQVAE-based methods often fail to represent novel motions faithfully using discrete tokens, which hampers their ability to generalize beyond seen data. Meanwhile, diffusion-based methods operating on continuous representations often lack fine-grained control over individual frames. To address these challenges, we propose a robust motion generation framework MoMADiff, which combines masked modeling with diffusion processes to generate motion using frame-level continuous representations. Our model supports flexible user-provided keyframe specification, enabling precise control over both spatial and temporal aspects of motion synthesis. MoMADiff demonstrates strong generalization capability on novel text-to-motion datasets with sparse keyframes as motion prompts. Extensive experiments on two held-out datasets and two standard benchmarks show that our method consistently outperforms state-of-the-art models in motion quality, instruction fidelity, and keyframe adherence. The code is available at: https://github.com/zzysteve/MoMADiff
Current advances in text-driven 3D scene editing tasks typically render the 3D representations into multi-view images and modify the images with the text instructions. Context consistency across multiple views and cross-modal consistency in the single-view are the keys to effective 3D editing. Accordingly, existing methods introduce additional image constraints and apply pre-trained 2D editing models. However, they fix the same text instruction across all views and freeze the pre-trained 2D model for single-view editing, leading to deficient modeling of 3D scene views and results in inconsistent generations with visual artifacts. To address these limitations, we introduce a discretized 3D view modeling method and a diffusion-based multi-view consistent editing pipeline for text-driven 3D gaussian splatting editing, abbreviated as D2Gaussian. Specifically, our approach constructs a codebook that encodes continuous 3D view information into discrete token embeddings to model the spatial feature expressions. Then, the token embeddings are proposed to guide and finetune the diffusion-based image editing model with the dynamic addition of control conditions, yielding a multi-view consistent editing pipeline. Finally, we introduce a 3D editing dataset generation approach along with a 3D-CLIP-SIM metric to form a benchmark, 3D-MagicBrush, to provide more diverse evaluation scenarios for future 3D editing works. Experiments demonstrate that our method achieves better visual results and multi-view consistency than previous state-of-the-art methods.
Instruction-based image editing enables intuitive modifications of images through natural language descriptions. However, existing models often struggle to accurately identify the target region, which refers to the area that should be modified. As a result, unintended changes may occur in non-target areas, where the original image should remain unchanged. To address this issue, we propose FoRE, an MLLM-guided framework that identifies the target region based on the given edit instruction and performs image editing using region-aware embeddings. Within FoRE, the Region-guided Edit Adapter projects these embeddings from the MLLM domain to the diffusion condition space. Subsequently, the Region-guided Refinement Module refines the projected features to enhance spatial accuracy prior to guiding the diffusion process. Through comprehensive evaluations, we demonstrate that FoRE significantly improves localization accuracy and instruction fidelity compared to existing approaches. By explicitly incorporating region-aware conditioning, our framework effectively bridges the gap between instruction comprehension and spatially precise image modifications, advancing the capabilities of instruction-based image editing.