Diffusion Transformer (DiT)-based models have significantly advanced image editing by encoding conditional images and integrating them into transformer layers. However, most edits involve modifying only small regions, while current methods uniformly process and denoise all tokens at every timestep, causing redundant computation and potentially degrading unchanged areas. This raises a fundamental question: Is it truly necessary to regenerate every region during editing? To address this, we propose SpotEdit, a training-free diffusion editing framework that selectively updates only the modified regions. SpotEdit comprises two key components: SpotSelector identifies stable regions via perceptual similarity and skips their computation by reusing conditional image features; SpotFusion adaptively blends these features with edited tokens through a dynamic fusion mechanism, preserving contextual coherence and editing quality. By reducing unnecessary computation and maintaining high fidelity in unmodified areas, SpotEdit achieves efficient and precise image editing.
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Sign language production aims to generate sign sequences from spoken language, where the generation of sign pose sequences from text is often treated as a significant task. However, due to the differences in grammatical rules and modalities between sign language pose sequences and spoken language text, it is rather challenging to convert text into sign poses (i.e., Text2Pose), while maintaining semantic consistency, motion accuracy and temporal coherence.In this paper, we focus on the Text2Pose task, and propose SignPR, a progressive diffusion framework that jointly models the structural and temporal properties of signing. Structurally, we perform progressive structural refinement: a structural VQVAE encodes each frame into semantic-aware and region-based discrete representations; the diffusion process first produces semantically consistent poses and then progressively refines motion details under text and semantic conditioning. Temporally, we introduce block-wise causal diffusion, which progressively enforces temporal coherence and enables iterative refinement to earlier generated segments, yielding smoother transitions and reduced jitter. Extensive experiments on widely used datasets demonstrate that SignPR achieves superior results compared with prior T2P methods across multiple metrics, producing pose sequences that are semantically faithful, motion-accurate, and temporally coherent.
Compositional Text-to-Image Generation Via Region-aware Bimodal Direct Preference Optimization
PDF ↗Despite the rapid progress of text-to-image (T2I) models, generating images that accurately reflect complex compositional prompts (covering attribute bindings, object relationships, counting) still remains challenging. To address this, we propose BIDPO, a framework to enhance T2I model's capability of compositional text-to-image generation. We begin by introducing an carefully designed pipeline to construct a large-scale preference dataset, BICOMP, with strictly quality control. Then, we extend Diffusion DPO to jointly optimize image and text preferences, which is shown to greatly effective in improving the models to follow complex text prompt in generation. To further enhance the models for fine-grained alignment, we employ a region-level guidance method to focus on regions relevant to compositional concepts. Experimental results demonstrate that our BIDPO substantially improves compositional fidelity, consistently outperforming prior methods across multiple benchmarks. Our approach highlights the potential of preference-based fine-tuning for complex text-to-image tasks, offering a flexible and scalable alternative to existing techniques. Code is available at https://github.com/anzeameol/BiDPO.
Old photos preserve invaluable historical memories, making their restoration and colorization highly desirable. While existing restoration models can address some degradation issues like denoising and scratch removal, they often struggle with accurate colorization.This limitation arises from the unique degradation inherent in old photos, such as faded brightness and altered color hues, which are different from modern photo distributions, creating a substantial domain gap during colorization. In this paper, we propose a novel old photo colorization framework based on the generative diffusion model FLUX. Our approach introduces a structure-color decoupling strategy that separates structure preservation from color restoration, enabling accurate colorization of old photos while maintaining structural consistency. We further enhance the model with a progressive Direct Preference Optimization (Pro-DPO) strategy, which allows the model to learn subtle color preferences through coarse-to-fine transitions in color augmentation. Additionally, we address the limitations of text-based prompts by introducing visual semantic prompts, which extract fine-grained semantic information directly from old photos, helping to eliminate the color bias inherent in old photos. Experimental results on both synthetic and real datasets demonstrate that our approach outperforms existing state-of-the-art colorization methods, including closed-source commercial models, producing high-quality and vivid colorization.
Robust and privacy-preserving indoor scene understanding remains a fundamental open problem. While optical sensors such as RGB and LiDAR offer high spatial fidelity, they suffer from severe occlusions and introduce privacy risks in indoor environments. In contrast, millimeter-wave (mmWave) radar preserves privacy and penetrates obstacles, but its inherently low spatial resolution makes reliable geometric reasoning difficult. We introduce RISE, the first benchmark and system for single static radar indoor scene understanding, jointly targeting layout reconstruction and object detection. RISE is built upon the key insight that multipath reflections--traditionally treated as noise--encode rich geometric cues. To exploit this, we propose a Bi-Angular Multipath Enhancement that explicitly models Angle of Arrival and Angle of Departure to recover secondary (ghost) reflections and reveal invisible structures. On top of these enhanced observations, a simulation-to-reality hierarchical diffusion framework transforms fragmented radar responses into complete layout reconstruction and object detection. Our benchmark contains 50,000 frames collected across 100 real indoor trajectories, forming the first large-scale dataset dedicated to single static radar-based indoor scene understanding. Extensive experiments show that RISE reduces the Chamfer distance by 60% (down to 16 cm) compared to the state of the art in mmWave layout reconstruction, and delivers the first mmWave-based object detection, achieving 58% IoU. These results establish RISE as a new foundation for geometry-aware and privacy-preserving indoor scene understanding using a single static radar. Our website and code are available at https://rise-cvpr.github.io.
Human motion generation from text prompts has made remarkable progress in recent years. However, existing methods primarily rely on either sequence-level or action-level descriptions due to the absence of fine-grained, part-level motion annotations. This limits their controllability over individual body parts. In this work, we construct a high-quality motion dataset with atomic, temporally-aware part-level annotations, leveraging the reasoning capabilities of large language models (LLMs). Unlike prior datasets that either provide synchronized part captions with fixed time segments or rely solely on global sequence labels, our dataset captures asynchronous and semantically distinct part movements at fine temporal resolution. Based on this dataset, we introduce a diffusion-based part-aware motion generation framework, namely FrankenMotion, where each body part is guided by its own temporally-structured textual prompt. This is, to our knowledge, the first work to provide atomic, temporally-aware part-level motion annotations and have a model that allows motion generation with both spatial (body part) and temporal (atomic action) control. Experiments demonstrate that FrankenMotion outperforms all previous baseline models adapted and retrained for our setting, and our model can compose motions unseen during training. Our code and dataset will be publicly available upon publication.
Recent advances in generative models have enabled the creation of high-fidelity human faces, yet constructing reliable virtual identities that preserve user privacy while supporting consistent and verifiable identity assignment remains challenging. In this paper, we propose a diffusion-based framework for generating traceable virtual identities that maintains stable identity semantics while preserving pose and expression. Our framework couples a virtual identity sampler that generates diverse yet consistent identity embeddings with a 3D geometric and expression conditioning module that preserves the pose and non-identity characteristics of the input face. In addition, we incorporate a lightweight latent watermarking mechanism that embeds an imperceptible identity signature during generation, enabling a user to verify ownership of the resulting virtual identity through a secure token without revealing their real facial appearance. Quantitative evaluations demonstrate that our method achieves high virtual identity consistency, strong pose and expression fidelity, and improved anonymity compared with prior works. These results validate the effectiveness of integrating virtual identity sampling, geometric conditioning, and latent watermarking into a single generative framework, and highlight the practical potential of our solution for constructing privacy-aware virtual identities.
Precise spatial control in diffusion-based style transfer remains challenging. This challenge arises because diffusion models treat style as a global feature and lack explicit spatial grounding of style representations, making it difficult to restrict style application to specific objects or regions. To our knowledge, existing diffusion models are unable to perform true localized style transfer, typically relying on handcrafted masks or multi-stage post-processing that introduce boundary artifacts and limit generalization. To address this, we propose an attention-supervised diffusion framework that explicitly teaches the model where to apply a given style by aligning the attention scores of style tokens with object masks during training. Two complementary objectives, a Focus loss based on KL divergence and a Cover loss using binary cross-entropy, jointly encourage accurate localization and dense coverage. A modular LoRA-MoE design further enables efficient and scalable multi-style adaptation. To evaluate localized stylization, we introduce the Regional Style Editing Score, which measures Regional Style Matching through CLIP-based similarity within the target region and Identity Preservation via masked LPIPS and pixel-level consistency on unedited areas. Experiments show that our method achieves mask-free, single-object style transfer at inference, producing regionally accurate and visually coherent results that outperform existing diffusion-based editing approaches.
DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer
PDF ↗Simulation is essential to the development and evaluation of autonomous robots such as self-driving vehicles. Neural reconstruction is emerging as a promising solution as it enables simulating a wide variety of scenarios from real-world data alone in an automated and scalable way. However, while methods such as NeRF and 3D Gaussian Splatting can produce visually compelling results, they often exhibit artifacts particularly when rendering novel views, and fail to realistically integrate inserted dynamic objects, especially when they were captured from different scenes. To overcome these limitations we introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings from such imperfect scenes into photorealistic, temporally consistent outputs. At its core is a single-step temporally-conditioned enhancer that is converted from a pretrained multi-step image diffusion model, capable of running in online simulators on a single GPU. The key to training it effectively, is a custom data curation pipeline that constructs synthetic-real pairs emphasizing appearance harmonization, artifact correction, and lighting realism. Experiments show that DiffusionHarmonizer substantially improves perceptual realism, being chosen by 84.28% of users in our comparative study over the second best method. Furthermore, it matches the temporal coherence of state-of-the art video models while maintaining the inference efficiency of single-step image models, offering a scalable and practical solution for photorealistic simulation in both research and production settings.
Bridging the modality gap between infrared and visible imagery is critical for cross-modal understanding and for enriching multimodal benchmarks. However, existing approaches remain confined to one-to-one mappings and are typically evaluated on unidirectional or closed-set scenarios. To address this challenge, we present SynthRGB-T, a unified framework for diverse and bidirectional image translation. Specifically, we formulate image translation as a vision-language guided denoising diffusion process, enabling flexible conditioning and open-world generalization. To enhance semantic alignment, a Visual Grounding Pipeline (VGP) is introduced to exploit the world knowledge of foundation models for fine-grained translation guidance. During the diffusion process, we propose to adopt a decoupling injection strategy to alleviate interference among multiple guidance. In addition, a Dual Conditional Cross-Attention (DCCA) module is designed to facilitate collaborative representation learning in latent space. SynthRGB-T is simple and versatile--capable of synthesizing diverse, high-fidelity data that substantially extends multimodal resources within the community. Comprehensive evaluations on multiple real-world benchmarks confirm that SynthRGB-T delivers superior performance and enhanced visual diversity over existing approaches.
We introduce SketchDeco, a training-free approach to sketch colourisation that bridges the gap between professional design needs and intuitive, region-based control. Our method empowers artists to use simple masks and colour palettes for precise spatial and chromatic specification, avoiding both the tediousness of manual assignment and the ambiguity of text-based prompts. We reformulate this task as a novel, training-free composition problem. Our core technical contribution is a guided latent-space blending process: we first leverage diffusion inversion to precisely "paint" user-defined colours into specified regions, and then use a custom self-attention mechanism to harmoniously blend these local edits with a globally consistent base image. This ensures both local colour fidelity and global harmony without requiring any model fine-tuning. Our system produces high-quality results in 15--20 inference steps on consumer GPUs, making professional-quality, controllable colourisation accessible.
Image-to-Video generation (I2V) animates a static image into a temporally coherent video sequence following textual instructions, yet preserving fine-grained object identity under changing viewpoints remains a persistent challenge. Unlike text-to-video models, existing I2V pipelines often suffer from appearance drift and geometric distortion, artifacts we attribute to the sparsity of single-view 2D observations and weak cross-modal alignment. Here we address this problem from both data and model perspectives. First, we curate ConsIDVid, a large-scale object-centric dataset built with a scalable pipeline for high-quality, temporally aligned videos, and establish ConsIDVid-Bench, where we present a novel benchmarking and evaluation framework for multi-view consistency using metrics sensitive to subtle geometric and appearance deviations. We further propose ConsID-Gen, a view-assisted I2V generation framework that augments the first frame with unposed auxiliary views and fuses semantic and structural cues via a dual-stream visual-geometric encoder as well as a text-visual connector, yielding unified conditioning for a Diffusion Transformer backbone. Experiments across ConsIDVid-Bench demonstrate that ConsID-Gen consistently outperforms in multiple metrics, with the best overall performance surpassing leading video generation models like Wan2.1 and HunyuanVideo, delivering superior identity fidelity and temporal coherence under challenging real-world scenarios. Our model and dataset are at https://myangwu.github.io/ConsID-Gen.
Transformer-based video diffusion models (VDMs) deliver state-of-the-art video generation quality but are constrained by the quadratic cost of self-attention, making long sequences and high resolutions computationally expensive. While linear attention offers sub-quadratic complexity, previous approaches have failed to match the expressiveness of softmax attention unless retrained at significant computational cost. We introduce Attention Surgery, an efficient framework that enables linear or hybrid attention in pretrained VDMs, eliminating the need for training from scratch. Inspired by recent advances in language models, our method combines a novel hybrid attention mechanism, mixing softmax and linear tokens, with a lightweight distillation and fine-tuning pipeline requiring only a few GPU-days. Additionally, we incorporate a cost-aware block-rate strategy to balance expressiveness and efficiency across layers. Applied to Wan2.1 1.3B, a state-of-the-art efficient transformer VDM and evaluated on VBench, VBench2.0 and a human preference study, Attention Surgery achieves competitive results. Furthermore, measurements of on-mobile latency, memory usage, and FLOPs demonstrate notable improvements in scaling behavior for longer videos.
FastHybrid: Accelerating Hybrid Autoregressive Image Generation with Lookahead and Guided Decoding
PDF ↗Autoregressive (AR) models have achieved remarkable success in natural language processing, yet their application to image generation faces significant challenges. When implementing VQ-based decoders for autoregressive image generation, the generated images typically preserve semantic information but struggle with fine-grained details. Recent hybrid AR image generation approaches address these issues by integrating diffusion models as decoder heads, enabling more high-fidelity generation. However, the diffusion-based denoising process introduces significant computational overhead during inference.To accelerate hybrid AR image generation, we propose the Lookahead Decoding Strategy, which integrates the strengths of autoregressive and diffusion models by separating the process into two complementary branches: semantic prediction and detail refinement. The autoregressive branch captures high-level semantic structures while refining coarse predictions made by the parallel. Furthermore, we introduce Guided Diffusion Sampling to steer the diffusion denoising trajectory, significantly reducing the number of denoising steps. Extensive experiments demonstrate that our approach provides an effective solution for accelerating hybrid AR image generation models.
Conventional monocular depth estimators suffer from visual ambiguities and nuisances. We demonstrate that language can improve the fidelity of estimates by providing additional information through text as a condition, thereby reducing the solution space for depth estimates. This conditional distribution is learned during text-to-image pre-training of diffusion models. To generate images under various viewpoints and layouts that reflect textual descriptions, the model implicitly encodes object shapes and their spatial relationships, comprising the 3-dimensional (3D) scene structure. We investigate the benefits of integrating text descriptions into the training and inference of the diffusion-based monocular depth estimators (MDE). We experiment with three different diffusion-based MDEs, namely Marigold, Lotus, and E2E-FT, and their variants. By training on HyperSim and Virtual KITTI, and evaluating on NYUv2, KITTI, ETH3D, ScanNet, and DIODE, we find that our strategy of integrating text into MDEs improves the overall accuracy of depth estimates, especially in small areas. The effect is also targeted in that improvements are more pronounced in specific regions described in the text. Naturally, this lends to iterative refinement of the depth estimate by providing additional descriptions of the 3D scene. Separately, we find that incorporating text can accelerate convergence of training and the inference diffusion trajectory.
InstantRetouch: Efficient and High-Fidelity Instruction-Guided Image Retouching with Bilateral Space
PDF ↗Language-guided photo retouching aims to adjust color and tone while preserving geometry and texture. Recently, diffusion-based retouching shows a superior visual quality, but often struggles with both fidelity issues due to its generative nature and efficiency because of its iterative sampling process.In this work, we propose an efficient and fidelity-preserving retouching method using bilateral space manipulation, which is both compact and content-decoupled. Specifically, instead of directly editing pixels or image latents, our model predicts a low-resolution bilateral grid of affine transforms, which are sliced using a learned guidance map and then applied to the full-resolution image. This approach yields both high fidelity and improved efficiency.To retain strong priors of a pretrained generative model, we distill a multi-step diffusion model into our bilateral grid framework using Variational Score Distillation, complemented by a prompt alignment loss to guide instruction-following behavior. Additionally, we introduce a new benchmark and evaluate our method across multiple dimensions: fidelity, instruction following, and efficiency.Compared to the latest retouch methods, like Gemini-2.5-Flash (Nano-Banana), our method can avoid content drift, significantly improve latency, and generate visually pleasing edits, while maintaining a high level of fidelity.
Visual Autoregressive (VAR) models have demonstrated competitive performance with diffusion models in image generation by adopting a "next-scale" prediction paradigm that significantly reduces inference steps. However, VAR's progressive multi-scale generation leads to severe memory overhead due to KV cache accumulation across all scales, limiting practical deployment. Existing solutions either require training and deploying multiple specialized models or sacrifice generation quality.We observe a critical scale-depth asymmetric dependency in VAR: small scales (low-resolution tokens) are highly sensitive to network depth and require deep layers to capture global semantic information, while large scales (high-resolution tokens) exhibit remarkable robustness to depth reduction.Motivated by this insight, we propose VARiant, a unified supernet framework that enables dynamic depth adjustment within a single model through parameter sharing. Our approach employs an even-spacing layer selection strategy to construct quality-preserving subnetworks, and introduces a dynamic-ratio progressive training strategy that gradually transitions from joint optimization (full network to subnetwork ratio 2:8) to subnetwork optimization (ratio 10:0), effectively resolving the inherent optimization conflicts between full network and subnetworks in supernet training.Extensive experiments on ImageNet demonstrate that our method achieves Pareto-optimal trade-offs between generation quality and inference efficiency: by using full depth (30 layers) for the first 7 scales and a 16-layer subnetwork (50% depth) for subsequent scales, we obtain 50% cache reduction and 1.8x inference speedup with minimal quality loss (FID increases by only 0.3).Unlike approaches requiring deployment of multiple models, our single-model solution eliminates deployment complexity, supports zero-cost runtime depth switching, and seamlessly integrates into standard transformer inference frameworks, making it highly practical for resource-constrained scenarios.
FRAMER: Frequency-Aligned Self-Distillation with Adaptive Modulation Leveraging Diffusion Priors for Real-World Image Super-Resolution
PDF ↗Real-image super-resolution (Real-ISR) seeks to recover HR images from LR inputs with mixed, unknown degradations. While diffusion models surpass GANs in perceptual quality, they under-reconstruct high-frequency (HF) details due to a low-frequency (LF) bias and a depth-wise "low-first, high-later" hierarchy. We introduce FRAMER, a plug-and-play training scheme that exploits diffusion priors without changing the backbone or inference. At each denoising step, the final-layer feature map teaches all intermediate layers. Teacher and student feature maps are decomposed into LF/HF bands via FFT masks to align supervision with the model's internal frequency hierarchy. For LF, an Intra Contrastive Loss (IntraCL) stabilizes globally shared structure. For HF, an Inter Contrastive Loss (InterCL) sharpens instance-specific details using random-layer and in-batch negatives. Two adaptive modulators, Frequency-based Adaptive Weight (FAW) and Frequency-based Alignment Modulation (FAM), reweight per-layer LF/HF signals and gate distillation by current similarity. Across U-Net and DiT backbones (e.g., Stable Diffusion 2, 3), FRAMER consistently improves PSNR/SSIM and perceptual metrics (LPIPS, NIQE, MANIQA, MUSIQ). Ablations validate the final-layer teacher and random-layer negatives. The code and project page will be publicly released.
Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimization, known camera calibration, or short frame windows, making them slow and impractical. We revisit this problem from a feedforward perspective and introduce Driving Gaussian Grounded Transformer (DGGT), a unified framework for pose-free dynamic scene reconstruction. We note that the existing formulations, treating camera pose as a required input, limit flexibility and scalability. Instead, we reformulate pose as an output of the model, enabling reconstruction directly from sparse, unposed images and supporting an arbitrary number of views for long sequences. Our approach jointly predicts per-frame 3D Gaussian maps and camera parameters, disentangles dynamics with a lightweight dynamic head, and preserves temporal consistency with a lifespan head that modulates visibility over time. A diffusion-based rendering refinement further reduces motion/interpolation artifacts and improves novel-view quality under sparse inputs. The result is a single-pass, pose-free algorithm that achieves state-of-the-art performance and speed. Trained and evaluated on large-scale driving benchmarks (Waymo, nuScenes, Argoverse2), our method outperforms prior work both when trained on each dataset and in zero-shot transfer across datasets, and it scales well as the number of input frames increases.
Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact latent spaces. However, while previous research has focused primarily on reconstruction accuracy and semantic alignment of the latent space, we observe that another critical factor, robustness to sampling perturbations, also plays a crucial role in determining generation quality. Through empirical and theoretical analyses, we show that the commonly used b-VAE-based tokenizers in latent diffusion models, tend to produce overly compact latent manifolds that are highly sensitive to stochastic perturbations during diffusion sampling, leading to visual degradation. To address this issue, we propose a simple yet effective solution that constructs a latent space robust to sampling perturbations while maintaining strong reconstruction fidelity. This is achieved by introducing a Variance Expansion loss that counteracts variance collapse and leverages the adversarial interplay between reconstruction and variance expansion to achieve an adaptive balance that preserves reconstruction accuracy while improving robustness to stochastic sampling. Extensive experiments demonstrate that our approach consistently enhances generation quality across different latent diffusion architectures, confirming that robustness in latent space is a key missing ingredient for stable and faithful diffusion sampling. Our project page: https://github.com/CVL-UESTC/VE-Loss .