We present a method for learning 3D spatial relationships between object pairs, referred to as object-object spatial relationships (OOR), by leveraging synthetically generated 3D samples from pre-trained 2D diffusion models. We hypothesize that images synthesized by 2D diffusion models inherently capture realistic OOR cues, enabling efficient collection of a 3D dataset to learn OOR for various unbounded object categories. Our approach synthesizes diverse images that capture plausible OOR cues, which we then uplift into 3D samples. Leveraging our diverse collection of 3D samples for the object pairs, we train a score-based OOR diffusion model to learn the distribution of their relative spatial relationships. Additionally, we extend our pairwise OORtomulti-object OOR by enforcing consistency across pairwise relations and preventing object collisions. Extensive experiments demonstrate the robustness of our method across various object-object spatial relationships, along with its applicability to 3D scene arrangement tasks and human motion synthesis using our OOR diffusion model.
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Polarization images facilitate image enhancement and 3D reconstruction tasks, but the limited accessibility of polarization cameras hinders their broader application. This gap drives the need for synthesizing photorealistic polarization images. The existing polarization simulator Mitsuba relies on a parametric polarization image formation model and requires extensive 3D assets covering shape and PBR materials, preventing it from generating large-scale photorealistic images. To address this problem, we propose PolarAnything, capable of synthesizing polarization images from a single RGB input with both photorealism and physical accuracy, eliminating the dependency on 3D asset collections. Drawing inspiration from the zero-shot performance of pretrained diffusion models, we introduce a diffusion-based generative framework with an effective representation strategy that preserves the fidelity of polarization properties. Experiments show that our model generates high-quality polarization images and supports downstream tasks like shape from polarization.
We present FaceLift, a novel feed-forward approach for generalizable high-quality 360-degree 3D head reconstruction from a single image. Our pipeline first employs a multi-view latent diffusion model to generate consistent side and back views from a single facial input, which then feed into a transformer-based reconstructor that produces a comprehensive 3D Gaussian splats representation. Previous methods for monocular 3D face reconstruction often lack full view coverage or view consistency due to insufficient multi-view supervision. We address this by creating a high-quality synthetic head dataset that enables consistent supervision across viewpoints. To bridge the domain gap between synthetic training data and real-world images, we propose a simple yet effective technique that ensures the view generation process maintains fidelity to the input by learning to reconstruct the input image alongside the view generation. Despite being trained exclusively on synthetic data, our method demonstrates remarkable generalization to real-world images. Through extensive qualitative and quantitative evaluations, we show that FaceLift outperforms state-of-the-art 3D face reconstruction methods on identity preservation, detail recovery and rendering quality.
Surface normal estimation serves as a cornerstone for a spectrum of computer vision applications. While numerous efforts have been devoted to static image scenarios, ensuring temporal coherence in video-based normal estimation remains a formidable challenge. Instead of merely augmenting existing methods with temporal components, we present NormalCrafter to leverage the inherent temporal priors of Video Diffusion Models (VDMs). We identify the reason for blurry predictions when directly applying VDMs and introduce Semantic Feature Regularization (SFR) to encourage the model to concentrate on geometric details by aligning diffusion features with fine-grained semantic cues. Moreover, we introduce a two-stage training protocol that leverages both latent and pixel space learning to preserve spatial accuracy while maintaining long temporal context. Extensive evaluations demonstrate the efficacy of our method, showcasing a superior performance in generating temporally consistent normal sequences with intricate details from diverse videos. Code and models are publicly available at https://normalcrafter.github.io/.
Video Temporal Grounding (VTG) confronts the challenge of bridging the semantic gap between concise textual queries and the rich complexity of video content, further compounded by the difficulty of capturing discriminative features without explicit target cues. To address these challenges, we propose Knowledge Diffusion Alignment (KDA), a framework that leverages the generative prowess of diffusion models. KDA introduces a multi-layer video knowledge extraction module alongside a background residual diffusion model that progressively prunes irrelevant background information from global video features, thereby distilling query-relevant moment knowledge enriched with visual context. By a three-stage training approach that harnesses annotated moment guidance, KDA guarantees that the extracted moment knowledge incorporates the discriminative features necessary for accurate localization. A knowledge prompt reasoning module facilitates the comprehensive interaction and utilization of moment knowledge and multimodal features. Moreover, we introduce a spans-enhanced decoder that selectively integrates spans from multi-modal features, capitalizing on intrinsic alignment cues. Comprehensive experiments on three datasets demonstrate performance that surpasses state-of-the-art methods, attesting to the effectiveness of the proposed framework.
In this paper, we introduce MeshMamba, a neural network model for learning 3D articulated mesh models by employing the recently proposed Mamba State Space Models (Mamba-SSMs). MeshMamba is efficient and scalable in handling a large number of input tokens, enabling the generation and reconstruction of body mesh models with more than 10,000 vertices, capturing clothing and hand geometries. The key to effectively learning MeshMamba is the serialization technique of mesh vertices into orderings that are easily processed by Mamba. This is achieved by sorting the vertices based on body part annotations or the 3D vertex locations of a template mesh, such that the ordering respects the structure of articulated shapes. Based on MeshMamba, we design 1) MambaDiff3D, a denoising diffusion model for generating 3D articulated meshes and 2) Mamba-HMR, a 3D human mesh recovery model that reconstructs a human body shape and pose from a single image. Experimental results showed that MambaDiff3D can generate dense 3D human meshes in clothes, with grasping hands, etc., and outperforms previous approaches in the 3D human shape generation task. Additionally, Mamba-HMR extends the capabilities of previous non-parametric human mesh recovery approaches, which were limited to handling body-only poses using around 500 vertex tokens, to the whole-body setting with face and hands, while achieving competitive performance in (near) real-time.
Generative models have made rapid progress in content creation, particularly in synthesizing artworks and capturing stylistic variation. However, most methods operate at the level of individual images, limiting their ability to reveal broader stylistic trends and temporal transitions. We address this by introducing a framework that models stylistic evolution as an optimal transport problem in a learned style space, using stochastic interpolants and dual diffusion implicit bridges to align artistic distributions across time without requiring paired data. A central contribution is a diverse dataset of over 650,000 artworks spanning 500 years, curated with metadata across multiple genres. Together, our method and dataset enable tracing long-range stylistic transitions and plausible futures of individual artworks, supporting fine-grained temporal analysis. This offers a new tool for modeling historical patterns in visual culture and opens up promising directions in visual understanding. Code and dataset: https://github.com/CompVis/Art-fm
SuMa: A Subspace Mapping Approach for Robust and Effective Concept Erasure in Text-to-Image Diffusion Models
PDF ↗The rapid growth of text-to-image diffusion models has raised concerns about their potential misuse in generat- ing harmful or unauthorized contents. To address these issues, several Concept Erasure methods have been pro- posed. However, most of them fail to achieve both robust- ness, i.e., the ability to robustly remove the target concept., and effectiveness, i.e., maintaining image quality. While few recent techniques successfully achieve these goals for NSFW concepts, none could handle narrow concepts such as copyrighted characters or celebrities. Erasing these nar- row concepts is critical in addressing copyright and legal concerns. However, erasing them is challenging due to their close distances to non-target neighboring concepts, requir- ing finer-grained manipulation. In this paper, we introduce Subspace Mapping (SuMa), a novel method specifically de- signed to achieve both robustness and effectiveness in eas- ing these narrow concepts. SuMa first derives a target sub- space representing the concept to be erased and then neu- tralizes it by mapping it to a reference subspace that mini- mizes the distance between the two. This mapping ensures the target concept is robustly erased while preserving im- age quality. We conduct extensive experiments with SuMa across four tasks: subclass erasure, celebrity erasure, artis- tic style erasure, and instance erasure and compare the results with current state-of-the-art methods. Our method achieves image quality comparable to approaches focused on effectiveness, while also yielding results that are on par with methods targeting completeness.
Large-scale text-to-image diffusion models have achieved remarkable success in image generation, thereby driving the development of stylized image generation technologies. Recent studies introduce style information by empirically replacing specific features in attention blocks with style features. However, the relationship between features and style remains unclear. In this paper, we systematically analyze the relationship between features in attention blocks and style. By quantifying the distribution discrepancy induced by style variations using the Wasserstein distance, we find that features in self-attention blocks exhibit high sensitivity to style compared to features in cross-attention blocks. Our analysis provides valuable insights into the contribution of different features to style. Based on our findings, we propose a novel Wasserstein Style Distribution Transform (WSDT) method, which generates stylized images by transforming the distribution of style-sensitive features to align with that of style features. WSDT applies channel adaptive distribution transform to ensure that information not related to the style is not introduced. Our approach is simple yet efficient, optimization-free, and can be seamlessly integrated into attention-based text-to-image diffusion models. Extensive experiments demonstrate the effectiveness of our approach in stylized image generation tasks.
Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text line emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns--encompassing both intra- and inter-word relationships--and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text-lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush.
We present FreeMorph, the first tuning-free method for image morphing that accommodates inputs with varying semantics or layouts. Unlike existing methods, which rely on fine-tuning pre-trained diffusion models and are limited by time constraints and semantic/layout discrepancies, FreeMorph delivers high-fidelity image morphing without extensive training. Despite its efficiency and potential, tuning-free methods still face challenges in maintaining high-quality image morphing due to the non-linear nature of the multi-step denoising process and bias inherited from the pre-trained diffusion model. In this paper, we introduce FreeMorph to address this challenge by integrating two key innovations. 1) We first propose a guidance-aware spherical interpolation design that incorporates the explicit guidance from the input images by modifying the self-attention modules, addressing identity loss, and ensuring directional transitions throughout the generated sequences. 2) We further introduce a step-oriented variation trend that blends self-attention modules derived from each input image to achieve controlled and consistent transitions that respect both input images. Our extensive evaluations demonstrate that FreeMorph outperforms existing methods with training that is 10X 50X faster, establishing a new state-of-the-art for image morphing.
Recent advancements in large language models (LLMs) have significantly improved their ability to generate natural and contextually appropriate text, enabling more human-like interactions. However, understanding and generating interactive human-like motion, especially involving coordinated interactive motion, remains a challenging problem due to its inherent complexity. To address this, we present MoLaM, the Interactive Motion-LAnguage Model, a unified architecture that jointly processes language and motion modalities for understanding, generating, and controlling interactive motions in multi-turn conversational settings. Unlike prior approaches limited to uni-directional tasks (e.g., text-to-motion or motion-to-text), MoLaM supports a wide range of bi-directional and multi-turn tasks. We also introduce Inter-MT2, a large-scale instruction tuning dataset containing 82.7K multi-turn interactive motion instructions and 153K motion samples, covering tasks such as editing, question answering, and story generation. Leveraging LLMs and motion diffusion models, MoLaM demonstrates strong performance across five interactive motion tasks: motion-to-text, text-to-motion, reaction generation, motion editing, and motion reasoning, outperforming or matching task-specific baselines with a single, unified model.
While diffusion models significantly improve the perceptual quality of super-resolved images, they usually require a large number of sampling steps, resulting in high computational costs and long inference times. Recent efforts have explored reasonable acceleration schemes by reducing the number of sampling steps. However, these approaches treat all regions of the image equally, overlooking the fact that regions with varying levels of reconstruction difficulty require different sampling steps. To address this limitation, we propose PatchScaler, an efficient patch-independent diffusion pipeline for single image super-resolution. Specifically, PatchScaler introduces a Patch-adaptive Group Sampling (PGS) strategy that groups feature patches by quantifying their reconstruction difficulty and establishes shortcut paths with different sampling configurations for each group. To further optimize the patch-level reconstruction process of PGS, we propose a texture prompt that provides rich texture conditional information to the diffusion model. The texture prompt adaptively retrieves texture priors for the target patch from a common reference texture memory. Extensive experiments show that our PatchScaler achieves favorable performance in both quantitative and qualitative evaluations, while significantly speeding up inference. Project page: https://github.com/yongliuy/PatchScaler.
Fix-CLIP: Dual-Branch Hierarchical Contrastive Learning via Synthetic Captions for Better Understanding of Long Text
PDF ↗CLIP has shown promising performance across many short-text tasks in a zero-shot manner. However, limited by the input length of the text encoder, CLIP struggles on under-stream tasks with long-text inputs (>77 tokens). To remedy this issue, we propose FIX-CLIP, which includes three novel modules: (1) A dual-branch training pipeline that aligns short and long texts with masked and raw images, respectively, which boosts the long-text representation while preserving the short-text ability. (2) Multiple learnable regional prompts with unidirectional masks in Transformer layers for regional information extraction. (3) A hierarchical feature alignment module in the intermediate encoder layers to promote the consistency of multi-scale features. Furthermore, we collect 30M images and utilize existing MLLMs to synthesize long-text captions for training. Extensive experiments show that FIX-CLIP achieves state-of-the-art performance on both long-text and short-text retrieval benchmarks. For downstream applications, we reveal that FIX-CLIP's text encoder delivers promising performance in a plug-and-play manner for diffusion models with long-text input. The code is available at https://github.com/bcwang-sjtu/Fix-CLIP.
Diffusion models have transformed image editing but struggle with precise depth-aware control, such as placing objects at a specified depth. Layered representations offer fine-grained control by decomposing an image into separate editable layers. However, existing methods simplistically represent a scene via a set of background and transparent foreground layers while ignoring the scene geometry - limiting their effectiveness for depth-aware editing. We propose Depth-Guided Layer Decomposition - a layering method that decomposes an image into foreground and background layers based on a user-specified depth value, enabling precise depth-aware edits. We further propose Feature Guided Layer Compositing - a zero-shot approach for realistic layer compositing by leveraging generative priors from pretrained diffusion models. Specifically, we guide the internal U-Net features to progressively fuse individual layers into a composite latent at each denoising step. This preserves the structure of individual layers while generating realistic outputs with appropriate color and lighting adjustments without a need for post-hoc harmonization models. We demonstrate our method on two key depth-aware editing tasks: 1) scene compositing by blending the foreground of one scene with the background of another at a specified depth, and; 2) object insertion at a user-defined depth. Our zero-shot approach achieves precise depth ordering and high-quality edits, surpassing specialized scene compositing and object placement baselines, as validated across benchmarks and user studies.
Human-object interaction (HOI) detection relies on fine-grained visual understanding to distinguish complex relationships between humans and objects. While recent generative diffusion models have demonstrated remarkable capability in learning detailed visual concepts through pixel-level generation, their potential for interaction-level relationship modeling remains largely unexplored. To bridge this gap, we propose a Visual Relation Diffusion model (VRDiff), which introduces dense visual relation conditions to guide interaction understanding. Specifically, we encode interaction-aware condition representations that capture both spatial responsiveness and contextual semantics of human-object pairs, conditioning the diffusion process purely on visual features rather than text-based inputs. Furthermore, we refine these relation representations through generative feedback from the diffusion model, enhancing HOI detection without requiring image synthesis. Extensive experiments on the HICO-DET benchmark demonstrate that VRDiff achieves competitive results under both standard and zero-shot HOI detection settings.
Advancements in diffusion models have enabled effortless image editing via text prompts, raising concerns about image security. Attackers with access to user images can exploit these tools for malicious edits. Recent defenses attempt to protect images by adding a limited noise in the pixel space to disrupt the functioning of diffusion-based editing models. However, the adversarial noise added by previous methods is easily noticeable to the human eye. Moreover, most of these methods are not robust to purification techniques like JPEG compression under a feasible pixel budget. We propose a novel optimization approach that introduces adversarial perturbations directly in the frequency domain by modifying the Discrete Cosine Transform (DCT) coefficients of the input image. By leveraging the JPEG pipeline, our method generates adversarial images that effectively prevent malicious image editing. Extensive experiments across a variety of tasks and datasets demonstrate that our approach introduces fewer visual artifacts while maintaining similar levels of edit protection and robustness to noise purification techniques.
Controllable scene generation could reduce the cost of diverse data collection substantially for autonomous driving. Prior works formulate the traffic layout generation as a predictive progress, either by denoising entire sequences at once or by iteratively predicting the next frame. However, full sequence denoising hinders online reaction, while the latter's short-sighted next-frame prediction lacks precise goal-state guidance. Further, the learned model struggles to generate complex or challenging scenarios due to a large number of safe and ordinary driving behaviors from open datasets. To overcome these, we introduce Nexus, a decoupled scene generation framework that improves reactivity and goal conditioning by simulating both ordinal and challenging scenarios from fine-grained tokens with independent noise states. At the core of the decoupled pipeline is the integration of a partial noise-masking training strategy and a noise-aware schedule that ensures timely environmental updates throughout the denoising process. To complement challenging scenario generation, we collect a dataset consisting of complex corner cases. It covers 540 hours of simulated data, including high-risk interactions such as cut-in, sudden braking, and collision. Nexus achieves superior generation realism while preserving reactivity and goal orientation, with a 40% reduction in displacement error. We further demonstrate that Nexus improves closed-loop planning by 20% through data augmentation and showcase its capability in safety-critical data generation.
PersonaCraft: Personalized and Controllable Full-Body Multi-Human Scene Generation Using Occlusion-Aware 3D-Conditioned Diffusion
PDF ↗We present PersonaCraft, a framework for controllable and occlusion-robust full-body personalized image synthesis of multiple individuals in complex scenes. Current methods struggle with occlusion-heavy scenarios and complete body personalization, as 2D pose conditioning lacks 3D geometry, often leading to ambiguous occlusions and anatomical distortions, and many approaches focus solely on facial identity. In contrast, our PersonaCraft integrates diffusion models with 3D human modeling, employing SMPLx-ControlNet, to utilize 3D geometry like depth and normal maps for robust 3D-aware pose conditioning and enhanced anatomical coherence. To handle fine-grained occlusions, we propose Occlusion Boundary Enhancer Network that exploits depth edge signals with occlusion-focused training, and Occlusion-Aware Classifier-Free Guidance strategy that selectively reinforces conditioning in occluded regions without affecting unoccluded areas. PersonaCraft can seamlessly be combined with Face Identity ControlNet, achieving full-body multi-human personalization and thus marking a significant advancement beyond prior approaches that concentrate only on facial identity. Our dual-pathway body shape representation with SMPLx-based shape parameters and textual refinement, enables precise full-body personalization and flexible user-defined body shape adjustments. Extensive quantitative experiments and user studies demonstrate that PersonaCraft significantly outperforms existing methods in generating high-quality, multi-person images with accurate personalization and robust occlusion handling.
MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments
PDF ↗We introduce a diffusion-based approach for generating privacy-preserving digital twins of multi-room indoor environments from depth images only. Central to our approach is a novel Multi-view Overlapped Scene Alignment with Implicit Consistency (MOSAIC) model that explicitly considers cross-view dependencies within the same scene in the probabilistic sense. MOSAIC operates through a multi-channel inference-time optimization that avoids error accumulation common in sequential or single-room constraints in panorama-based approaches. MOSAIC scales to complex scenes with zero extra training and provably reduces the variance during denoising process when more overlapping views are added, leading to improved generation quality. Experiments show that MOSAIC outperforms state-of-the-art baselines on image fidelity metrics in reconstructing complex multi-room environments.