Although current work of text-to-Image generation can preliminarily generate images from the descriptions of human-object interactions, it fails to consider the emotions involved in human-object interactions. While people often experience emotions when using objects or interacting with them. Therefore, in this paper, we propose Emotional Interaction Generation task, a novel image generation task, which generates emotionally expressive human-object interaction images from given prompts, human-object interaction (HOI) region, and emotions. First, we construct a new emotional interaction dataset, called EmotionHOI, which including 47,776 images with content prompt, emotions and human-object interaction bounding box. Second, we propose an emotion-aware text-to-image diffusion model, named EmIT, for emotional interaction generation. Specifically, EmIT consists of three components: (1) an emotion interaction tokenizer that encodes subject, object, action, and emotion into structured tokens; (2) an Emo-Interaction Self-Attention that preliminarily guides the latent space to conduct hybrid learning with emotional interaction tokens; and (3) a Hierarchical Emotion-Visual Cross-Attention that further focus on grounding affect-such as pose, gaze, or interaction intensity-into specific spatial regions and capture subtle emotional variations. These components jointly model interaction semantics and emotional context, enabling EmIT to generate images that are both behaviorally coherent and emotionally expressive. Experimental results on the EmotionHOI dataset demonstrate the superiority of the proposed model.
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Recent advancements in diffusion models (DMs) have been propelled by alignment methods that post-train models to better conform to human preferences. However, these approaches typically require computation-intensive training of a base model and a reward model, which not only incurs substantial computational overhead but may also compromise model accuracy and training efficiency. To address these limitations, we propose Inversion-DPO, a novel alignment framework that circumvents reward modeling by reformulating Direct Preference Optimization (DPO) with DDIM inversion for DMs. Our method conducts intractable posterior sampling in Diffusion-DPO with the deterministic inversion from winning and losing samples to noise and thus derive a new post-training paradigm. This paradigm eliminates the need for auxiliary reward models or inaccurate appromixation, significantly enhancing both precision and efficiency of training. We apply Inversion-DPO to a basic task of text-to-image generation and a challenging task of compositional image generation. Extensive experiments show substantial performance improvements achieved by Inversion-DPO compared to existing post-training methods and highlight the ability of the trained generative models to generate high-fidelity compositionally coherent images. For the post-training of compostitional image geneation, we curate a paired dataset consisting of 11,140 images with complex structural annotations and comprehensive scores, designed to enhance the compositional capabilities of generative models. Inversion-DPO explores a new avenue for efficient, high-precision alignment in diffusion models, advancing their applicability to complex realistic generation tasks. Our code is available at https://github.com/MIGHTYEZ/Inversion-DPO
Creating a realistic animatable avatar from a single static portrait remains challenging. Existing approaches often struggle to capture subtle facial expressions, the associated global body movements, and the dynamic background. To address these limitations, we propose a novel framework that leverages a pretrained video diffusion Transformer model to generate high-fidelity, coherent talking portraits with controllable motion dynamics. At the core of our work is a dual-stage audio-visual alignment strategy. In the first stage, we employ a clip-level training scheme to establish coherent global motion by aligning audio-driven dynamics across the entire scene, including the reference portrait, contextual objects, and background. In the second stage, we refine lip movements at the frame level using a lip-tracing mask, ensuring precise synchronization with audio signals. To preserve identity without compromising motion flexibility, we replace the commonly used reference network with a lightweight cross-attention module that effectively maintains facial consistency throughout the video. Furthermore, we integrate a motion intensity modulation module that explicitly controls facial keypoints and body joint trajectories, enabling fine-grained manipulation of portrait movements beyond mere lip motion. Extensive experimental results show that our proposed approach achieves higher quality with better realism, coherence, motion intensity, and identity preservation. Our demo, code, models can be found on this page: https://fantasy-amap.github.io/fantasy-talking/.
Current semantic segmentation models typically require a substantial amount of manually annotated data, a process that is both time-consuming and resource-intensive. Alternatively, leveraging advanced text-to-image models such as Midjourney and Stable Diffusion has emerged as an efficient strategy, enabling the automatic generation of synthetic data in place of manual annotations. However, previous methods have been limited to generating single-instance images, as the generation of multiple instances with Stable Diffusion has proven unstable and masks can be significantly affected by occlusion between different objects. To overcome this limitation and broaden the variety of synthetic datasets, we propose a novel framework, Free-Mask. It combines a Diffusion Model for segmentation with advanced image editing capabilities, allowing the insertion of multiple objects into images through text-to-image models. In addition, we introduce a new active learning paradigm that benefits both model generalization and data optimization. Our method enables the creation of realistic datasets that closely reflect open-world environments while generating accurate segmentation masks. Our code is released on GitHub.
Despite recent advancements in text-to-image models, achieving semantically accurate images in text-to-image diffusion models is a persistent challenge. While existing initial latent optimization methods have demonstrated impressive performance, we identify two key limitations: (a) attention neglect, where the synthesized image omits certain subjects from the input prompt because they do not have a designated region in the self-attention map despite despite having a high-response cross-attention, and (b) attention interference, where the generated image has mixed-up properties of multiple subjects because of a conflicting overlap between cross- and self-attention maps of different subjects. To address these limitations, we introduce CoCoNO, a new algorithm that optimizes the initial latent by leveraging the complementary information within self-attention and cross-attention maps. We first identify subject-specific regions from the self-attention map and term them attention zones. Our method then introduces two new loss functions: the attention contrast loss, which minimizes undesirable overlap by ensuring each attention zone is exclusively linked to a specific subject's cross attention map, and the attention complete loss, which maximizes the activation within these attention zones to guarantee that each subject is fully and distinctly represented. Our approach operates within a noise optimization framework, avoiding the need to retrain base models. Through extensive experiments on multiple benchmarks, we demonstrate that CoCoNO significantly improves text-image alignment and outperforms the current state of the art.
While diffusion models advance text-to-motion generation, their static semantic conditioning ignores temporal-frequency demands: early denoising requires structural semantics for motion foundations while later stages need localized details for text alignment. This mismatch mirrors biological morphogenesis where developmental phases demand distinct genetic programs. Inspired by epigenetic regulation governing morphological specialization, we propose (ANT), an Adaptive Neural Temporal-Aware architecture. ANT orchestrates semantic granularity through: (i) Semantic Temporally Adaptive (STA) Module: Automatically partitions denoising into low-frequency structural planning and high-frequency refinement via spectral analysis. (ii) Dynamic Classifier-Free Guidance scheduling (DCFG): Adaptively adjusts conditional to unconditional ratio enhancing efficiency while maintaining fidelity. Extensive experiments show that ANT can be applied to various baselines, significantly improving model performance, and achieving state-of-the-art semantic alignment on StableMoFusion. Code can be found on https://github.com/CCSCovenant/ANT.
Blind face restoration (BFR) is a fundamental and challenging problem in computer vision. To faithfully restore high-quality (HQ) photos from poor-quality ones, recent research endeavors predominantly rely on facial image priors from the powerful pretrained text-to-image (T2I) diffusion models. However, such priors often lead to the incorrect generation of non-facial features and insufficient facial details, thus rendering them less practical for real-world applications. In this paper, we propose a novel framework, namely AuthFace that achieves highly authentic face restoration results by exploring a face-oriented generative diffusion prior. To learn such a prior, we first collect a dataset of 1.5K high-quality images, with resolutions exceeding 8K, captured by professional photographers. Based on the dataset, we then introduce a novel face-oriented restoration-tuning pipeline that fine-tunes a pretrained T2I model. Identifying key criteria of quality-first and photography-guided annotation, we involve the retouching and reviewing process under the guidance of photographers for high-quality images that show rich facial features. The photography-guided annotation system fully explores the potential of these high-quality photographic images. In this way, the potent natural image priors from pretrained T2I diffusion models can be subtly harnessed, specifically enhancing their capability in facial detail restoration. Moreover, to minimize artifacts in critical facial areas, such as eyes and mouth, we propose a time-aware latent facial feature loss to learn the authentic face restoration process. Extensive experiments on the synthetic and real-world BFR datasets demonstrate the superiority of our approach.
In this paper, we tackle a new task of 3D object synthesis, where a 3D model is composited with another object category to create a novel 3D model. However, most existing text/image/3D-to-3D methods struggle to effectively integrate multiple content sources, often resulting in inconsistent textures and inaccurate shapes. To overcome these challenges, we propose a straightforward yet powerful approach, category+3D-to-3D (C33D), for generating novel and structurally coherent 3D models. Our method begins by rendering multi-view images and normal maps from the input 3D model, then generating a novel 2D object using adaptive text-image harmony (ATIH) with the front-view image and another object category as inputs. To ensure texture consistency, we introduce texture multi-view diffusion, which refines the textures of the remaining multi-view RGB images based on the novel 2D object. For enhanced shape accuracy, we propose shape multi-view diffusion to improve the 2D shapes of both the multi-view RGB images and the normal maps, also conditioned on the novel 2D object. Finally, these outputs are used to reconstruct a complete and novel 3D model. Extensive experiments demonstrate the effectiveness of our method, yielding impressive 3D creations.
The rapid advancement of diffusion models holds the promise of revolutionizing the application of VR and AR technologies, which typically require scene-level 4D assets for user experience. Nonetheless, existing diffusion models predominantly concentrate on modeling static 3D scenes or object-level dynamics, constraining their capacity to provide truly immersive experiences. To address this issue, we propose HoloTime, a framework that integrates video diffusion models to generate panoramic videos from a single prompt or reference image, along with a 360-degree 4D scene reconstruction method that seamlessly transforms the generated panoramic video into 4D assets, enabling a fully immersive 4D experience for users. Specifically, to tame video diffusion models for generating high-fidelity panoramic videos, we introduce the 360World dataset, the first comprehensive collection of panoramic videos suitable for downstream 4D scene reconstruction tasks. With this curated dataset, we propose Panoramic Animator, a two-stage image-to-video diffusion model that can convert panoramic images into high-quality panoramic videos. Following this, we present Panoramic Space-Time Reconstruction, which leverages a space-time depth estimation method to transform the generated panoramic videos into 4D point clouds, enabling the optimization of a holistic 4D Gaussian Splatting representation to reconstruct spatially and temporally consistent 4D scenes. To validate the efficacy of our method, we conducted a comparative analysis with existing approaches, revealing its superiority in both panoramic video generation and 4D scene reconstruction. This demonstrates our method's capability to create more engaging and realistic immersive environments, thereby enhancing user experiences in VR and AR applications.
Automating the synthesis of coordinated bimanual piano performances poses significant challenges, particularly in capturing the intricate choreography between the hands while preserving their distinct kinematic signatures. In this paper, we propose a dual-stream neural framework designed to generate synchronized hand gestures for piano playing from audio input, addressing the critical challenge of modeling both hand independence and coordination. Our framework introduces two key innovations: (i) a decoupled diffusion-based generation framework that independently models each hand's motion via dual-noise initialization, sampling distinct latent noise for each while leveraging a shared positional condition, and (ii) a Hand-Coordinated Asymmetric Attention (HCAA) mechanism suppresses symmetric (common-mode) noise to highlight asymmetric hand-specific features, while adaptively enhancing inter-hand coordination during denoising. Comprehensive evaluations demonstrate that our framework outperforms existing state-of-the-art methods across multiple metrics.
In this paper, a multi-modal model based 3D pop-out video generation framework (CP3) is proposed to solve the shortcomings of the existing video generation technology for accurate control of 3D pop-out effects. 3D pop-out effects create an immersive visual experience by changing the disparity of a particular object so that it appears beyond the screen. However, although software has made some progress in this area, there is currently no effective way to accurately control 3D pop-out effects and generate high-quality video. In addition, the lack of high-quality 3D pop-out effect data sets is also one of the bottlenecks in the field. Therefore, the CP3 framework proposed in this paper utilizes multi-modal models to help 3D video creators make 3D pop-out effects, enhance the audience's sense of immersion and visual comfort, and thus promote the development of 3D effect generation technology. To support the training and evaluation of this framework, a new dataset containing 37000 frames of pop-out effects is constructed, such as text guidance, segmentation results, depth maps, optical flow, and the trajectory of the pop-out target. Through the 3D UNet model based on the potential de-noising diffusion mechanism, combined with the 3D-try module in the CP3 framework and Mask Encoder, this paper has achieved remarkable results in the generation of 3D pop-out effect videos. The results of the experiment show that the CP3 framework demonstrates its advantages in generating immersive 3D pop-out effects in comparison to existing technologies.
The high computational cost and slow inference time are major obstacles to deploying Video Diffusion Models (VDMs). To overcome this, we introduce a new Video Diffusion Model Compression approach using individual content and motion dynamics preserved pruning and consistency loss. First, we empirically observe that deeper VDM layers are crucial for maintaining the quality of motion dynamics (e.g., coherence of the entire video), while shallower layers are more focused on individual content (e.g., individual frames). Therefore, we prune redundant blocks from the shallower layers while preserving more of the deeper layers, resulting in a lightweight VDM variant called VDMini. Moreover, we propose an Individual Content and Motion Dynamics (ICMD) Consistency Loss to gain comparable generation performance as larger VDM to VDMini. In particular, we first use the Individual Content Distillation (ICD) Loss to preserve the consistency in the features of each generated frame between the teacher and student models. Next, we introduce a Multi-frame Content Adversarial (MCA) Loss to enhance the motion dynamics across the generated video as a whole. This method significantly accelerates inference time while maintaining high-quality video generation. Extensive experiments demonstrate the effectiveness of our VDMini on two important video generation tasks, Text-to-Video (T2V) and Image-to-Video (I2V), where we respectively achieve an average 2.5 ×, 1.4 ×, and 1.25 × speed up for the I2V method SF-V, the T2V method T2V-Turbo-v2, and the T2V method HunyuanVideo, while maintaining the quality of the generated videos on several benchmarks including UCF101, VBench-T2V, and VBench-I2V.
Recent advances in diffusion models have endowed talking head synthesis with subtle expressions and vivid head movements, but have also led to slow inference speed and insufficient control over generated results. To address these issues, we propose Ditto, a diffusion-based talking head framework that enables fine-grained controls and real-time inference. Specifically, we utilize an off-the-shelf motion extractor and devise a diffusion transformer to generate representations in a specific motion space. We optimize the model architecture and training strategy to address the issues in generating motion representations, including insufficient disentanglement between motion and identity, and large internal discrepancies within the representation. Besides, we employ diverse conditional signals while establishing a mapping between motion representation and facial semantics, enabling control over the generation process and correction of the results. Moreover, we jointly optimize the holistic framework to enable streaming processing, real-time inference, and low first-frame delay, offering functionalities crucial for interactive applications such as AI assistants. Extensive experimental results demonstrate that Ditto generates compelling talking head videos and exhibits superiority in both controllability and real-time performance.
Recent DDS-based video editing methods have presented remarkable potential by enhancing traditional diffusion models. However, these methods are limited by the MSE-based isolated comparison of noises, leading to issues such as numerical sensitivity and local structure perception deficiency. Additionally, the inherent uncertainty introduced by the noise injection process in diffusion models further hinders the improvement of editing performance. To address these limitations, we propose an Evidential Video Editing (EVE) framework, which normalizes noise vectors into probability distributions, enhancing the comparability of element relationships. By leveraging evidential deep learning, EVE employs Dirichlet distributions to establish distribution-based probabilistic modeling, overcoming the constraints of single deterministic normalization probabilities. Furthermore, we introduce an uncertainty-guided local optimization strategy to capture the local uncertainty of noise and preserve local structural details, thereby improving editing precision. Extensive experiments demonstrate that our method achieves state-of-the-art performance in video editing.
Current reaction generation studies often assume the homogeneity of all reactor body joints in the end-to-end motion generation while neglecting the physical contact information, resulting in evident joint mismatches in both temporal and spatial dimensions. In this paper, we introduce our method, Reactffusion, which addresses the reaction joint mismatch issue by explicitly leveraging the guidance from the actor-reactor physical contacts. At the mathematical modeling level, we reformulate the contact-guided reaction generation as a multi-task problem, divided into two sub-problems: contact information learning and reaction generation with physical constraints. Specifically, given the actor motion sequence, we first introduce a Contact Prediction Module (CPM), which adopts a spatial and temporal attentive mechanism to forecast the contact map, indicating the timing and the location of the potential joint collisions. Then, we employ the contact map as an explicit guide to rectify the sampling distribution in the denoising process of the proposed diffusion network. The comprehensive evaluations prove our method can achieve state-of-the-art performance compared with other reaction generation methods across multiple public benchmarks. Furthermore, the contact map predicted by the CPM can also effectively boost other baselines as an extra plug-in.
Personalized image generation has emerged as a promising direction in multimodal content creation. It aims to synthesize images tailored to individual style preferences (e.g. color schemes, character appearances, layout) and semantic intentions (e.g. emotion, action, scene contexts) by leveraging user-interacted history images and multimodal instructions. Despite notable progress, existing methods -- whether based on diffusion models, large language models, or Large Multimodal Models (LMMs) -- struggle to accurately capture and composite user style preferences and semantic intentions. In particular, the state-of-the-art LMM-based method suffers from the entanglement of visual features, leading to Guidance Collapse, where the generated images fail to preserve user-preferred styles or reflect the specified semantics. To address these limitations, we introduce DRC, a novel personalized image generation framework that enhances LMMs through Disentangled Representation Composition. DRC explicitly extracts user style preferences and semantic intentions from history images and the reference image, respectively, to form user-specific latent instructions that guide image generation within LMMs. Specifically, it involves two critical learning stages: 1) Disentanglement learning, which employs a dual-tower disentangler to explicitly separate style and semantic features, optimized via a reconstruction-driven paradigm with difficulty-aware importance sampling; and 2) Personalized modeling, which applies semantic-preserving augmentations to effectively adapt the disentangled representations for robust personalized generation. Extensive experiments on two benchmarks demonstrate that DRC shows competitive performance while effectively mitigating the guidance collapse issue, underscoring the importance of disentangled representation learning for controllable and effective personalized image generation.
Diffusion Models (DMs) have revolutionized Text-to-Image (T2I) generation, yet inherent dataset biases often result in skewed representations across demographics, perpetuating stereotypes and social inequities. Existing debiasing approaches primarily focus on the text processing component, overlooking the intricate biases in the diffusion model's U-Net architecture. This paper presents a novel approach to addressing these biases through a causal analysis of bias disentanglement within the U-Net architecture. We introduce the Contrast Neuron Sensitivity Metric, which enables precise identification of neurons sensitive to bias, allowing for targeted interventions. Our debiasing paradigm fine-tunes these identified neurons with a combination of distribution and semantic loss, requiring only 0.2M parameters to be adjusted, which is far less than prior methods. Experiments show that our method effectively removes gender and race biases and maintains the diversity distribution of images. It enables both absolute fairness and relative adjustments by modifying target attribute distributions (e.g., young:old = 7:3). Furthermore, our approach is scalable, allowing simultaneous fine-tuning across multiple biases, and achieves good bias reduction even with non-templated prompts. The code is available on https://github.com/FanQi-AI/Debias.
Diffusion Transformer (DiT) is a crucial method for content generation. However, it needs a lot of time to sample. Many studies have attempted to use caching to reduce the time consumption of sampling. Existing caching methods accelerate generation by reusing DiT features from the previous time step and skipping calculations in the next, but they tend to locate and cache low-error modules without focusing on reducing caching-induced errors, resulting in a sharp decline in generated content quality when increasing caching intensity. To solve this problem, we propose the Error-Optimized Cache (EOC). This method introduces three key improvements: (1) Prior knowledge extraction: Extract and process the caching differences; (2) A judgment method for cache optimization: Determine whether certain caching steps need to be optimized; (3) Cache optimization: reduce caching errors. Experiments show that this algorithm significantly reduces the error accumulation caused by caching, especially excessive caching. On the ImageNet dataset, without substantially increasing the computational load, this method improves the FID↓ of the generated images when the rule-based model FORA has a caching level of 75%, 50%, and 25%, and the training-based model Learning-to-cache has a caching level of 22%. Specifically, the FID↓ values change from 30.454 to 21.690 (28.8%), from 6.857 to 5.821 (15.1%), from 3.870 to 3.692 (4.6%), and from 3.539 to 3.451 (2.5%) respectively. Code is available at https://github.com/qiujx0520/EOC_MM2025.git.
With the advancement of autonomous driving technology, there is an increasing demand for high-quality and diverse images of road traffic scenes. Style transfer techniques can be employed to synthesize large-scale datasets. However, existing image style transfer methods often exhibit suboptimal performance in transferring styles for road scenes, frequently struggling to maintain structural consistency. In this paper, we propose a novel network architecture for unsupervised image style transfer named SVDGNet. This architecture dynamically adjusts the weights of different image regions during model training by calculating the Shapley values for the source and target domain images. We also employ a pre-trained diffusion model to generate better-stylized images. The experimental results demonstrate that the proposed method achieves better performance compared to the existing methods, which can preserve the structural consistency of the source domain images while providing impressive style transfer results.
The rapid development of music diffusion models has provided diverse paths for music creation transformations. However, existing methods still lack continuous strength regulation over stylistic attributes-specifically, they cannot achieve scalable adjustment of intensity (e.g., smooth transitions between ''gentle'' and ''intense'' jazz) while preserving spectral-temporal coherence. To address this, we propose RLScale-LoRA, a two-stage finetuning framework built on a structurally modified low-rank adaptation (LoRA) architecture with scale layers. In Stage 1, we finetune the modified LoRA to specialize in capturing attribute-aware latent spaces on unseen/seen music data. Stage 2 trains lightweight scale layers via proximal policy optimization (PPO), where reward functions enforce intermediate spectral-temporal state stability. Therefore, our RLScale-LoRA achieves precise, continuous music attribute transformations. Extensive experiments on Mtg-Jamendo and MedleyMD-Prompts datasets demonstrate RLScale-LoRA's superiority in granularity and coherence.