Generating naturalistic and nuanced listener motions for extended interactions remains an open problem. Existing methods often rely on low-dimensional motion codes for facial behavior generation followed by photorealistic rendering, limiting both visual fidelity and expressive richness. To address these challenges, we introduce DiTaiListener, powered by a video diffusion model with multimodal conditions. Our approach first generates short segments of listener responses conditioned on the speaker's speech and facial motions with DiTaiListener-Gen. It then refines the transitional frames via DiTaiListener-Edit for a seamless transition. Specifically, DiTaiListener-Gen adapts a Diffusion Transformer (DiT) for the task of listener head portrait generation by introducing a Causal Temporal Multimodal Adapter (CTM-Adapter) to process speakers' auditory and visual cues. CTM-Adapter integrates speakers' input in a causal manner into the video generation process to ensure temporally coherent listener responses. For long-form video generation, we introduce DiTaiListener-Edit, a transition refinement video-to-video diffusion model. The model fuses video segments into smooth and continuous videos, ensuring temporal consistency in facial expressions and image quality when merging short video segments produced by DiTaiListener-Gen. Quantitatively, DiTaiListener achieves the state-of-the-art performance on benchmark datasets in both photorealism (+73.8% in FID on RealTalk) and motion representation (+6.1% in FD metric on VICO) spaces. User studies confirm the superior performance of DiTaiListener, with the model being the clear preference in terms of feedback, diversity, and smoothness, outperforming competitors by a significant margin. See the project page for more results: https://ihp-lab.github.io/DiTaiListener/
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In this paper, we introduce DimensionX, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins with the insight that both the spatial structure of a 3D scene and the temporal evolution of a 4D scene can be effectively represented through sequences of video frames. While recent video diffusion models have shown remarkable success in producing vivid visuals, they face limitations in directly recovering 3D/4D scenes due to poor spatial and temporal controllability during generation. To overcome this difficulty, we propose ST-Director, which decouples spatial and temporal factors in video diffusion by learning dimension-aware directors from dimension-variant data. This decoupled video diffusion enables precise manipulation of spatial structures and temporal dynamics, allowing us to reconstruct both 3D and 4D representations from sequential frames by combining spatial and temporal dimensions. Additionally, to bridge the gap between generated videos and real-world scenes, we introduce a trajectory-aware mechanism for 3D generation and an identity-preserving denoising strategy for 4D generation, respectively. Extensive experiments on various real-world and synthetic datasets demonstrate that DimensionX achieves state-of-the-art performance in decoupled video generation, as well as 3D and 4D scene generation.
In the recent development of conditional diffusion models still require heavy supervised fine-tuning for performing control on a category of tasks. Training-free conditioning via guidance with off-the-shelf models is a favorable alternative to avoid further fine-tuning on the base model. However, the existing training-free guidance frameworks either have heavy memory requirements or offer sub-optimal control due to rough estimation. These shortcomings limit the applicability to control diffusion models that require intense computation, such as Text-to-Video (T2V) diffusion models. In this work, we propose Taming Inference Time Alignment for Guided Text-to-Video Diffusion Model, so-called TITAN-Guide, which overcomes memory space issues, and provides more optimal control in the guidance process compared to the counterparts. In particular, we develop an efficient method for optimizing diffusion latents without backpropagation from a discriminative guiding model. In particular, we study forward gradient descents for guided diffusion tasks with various options on directional directives. In our experiments, we demonstrate the effectiveness of our approach in efficiently managing memory during latent optimization, while previous methods fall short. Our proposed approach not only minimizes memory requirements but also significantly enhances T2V performance across a range of diffusion guidance benchmarks. Code, models, and demo are available at https://titanguide.github.io.
The natural world presents complex organic structures, such as tree canopies, that humans can interpret even when only partially visible.Understanding tree structures is key for forest monitoring, orchard management, and automated harvesting applications.However, reconstructing tree topologies from sensor data, called tree skeletonization, remains a challenge for computer vision approaches. Traditional methods for tree skeletonization rely on handcrafted features, regression, or generative models, whereas recent advances focus on deep learning approaches. Existing methods often struggle with occlusions caused by dense foliage, limiting their applicability over the annual vegetation cycle. Furthermore, the lack of real-world data with reference information limits the evaluation of these methods to synthetic datasets, which does not validate generalization to real environments.In this paper, we present a novel approach for tree skeletonization that combines a generative denoising diffusion probabilistic model for predicting node positions and branch directions with a classical minimum spanning tree algorithm to infer tree skeletons from 3D point clouds, even with strong occlusions. Additionally, we provide a dataset of an apple orchard with 280 trees scanned 10 times during the growing season with corresponding reference skeletons, enabling quantitative evaluation. Experiments show the superior performance of our approach on real-world data and competitive results compared to state-of-art approaches on synthetic benchmarks.
Scene text recognition (STR) suffers from challenges of either less realistic synthetic training data or the difficulty of collecting sufficient high-quality real-world data, limiting the effectiveness of trained models. Meanwhile, despite producing holistically appealing text images, diffusion-based visual text generation methods struggle to synthesize accurate and realistic instance-level text at scale. To tackle this, we introduce TextSSR: a novel pipeline for Synthesizing Scene Text Recognition training data. TextSSR targets three key synthesizing characteristics: accuracy, realism, and scalability. It achieves accuracy through a proposed region-centric text generation with position-glyph enhancement, ensuring proper character placement. It maintains realism by guiding style and appearance generation using contextual hints from surrounding text or background. This character-aware diffusion architecture enjoys precise character-level control and semantic coherence preservation, without relying on natural language prompts. Therefore, TextSSR supports large-scale generation through combinatorial text permutations. Based on these, we present TextSSR-F, a dataset of 3.55 million quality-screened text instances. Extensive experiments show that STR models trained on TextSSR-F outperform those trained on existing synthetic datasets by clear margins on common benchmarks, and further improvements are observed when mixed with real-world training data. Code is available at https://github.com/YesianRohn/TextSSR.
Generative models have rapidly evolved to generate realistic outputs. However, their synthetic outputs increasingly challenge the clear distinction between natural and AI-generated content, necessitating robust watermarking techniques to mark synthetic images. Watermarks are typically expected to preserve the integrity of the target image, withstand removal attempts, and prevent unauthorized insertion of the watermark pattern onto unrelated images. To address this need, recent methods embed persistent watermarks into images produced by diffusion models using the initial noise of the diffusion process. Yet, to do so, they either distort the distribution of generated images or require searching a large dictionary of candidate noise patterns for detection. In this paper, we propose a novel watermarking method that embeds semantic information about the generated image into the noise pattern, enabling a distortion-free watermark that can be verified without requiring a database of key patterns. Instead, the key pattern can be inferred from the semantic embedding of the image using locality-sensitive hashing. Furthermore, conditioning the watermark detection on the original image content improves its robustness against forgery attacks. To demonstrate that, we consider two largely overlooked attack strategies: (i) an attacker extracting the initial noise and generating a novel image with the same pattern; (ii) an attacker inserting an unrelated (potentially harmful) object into a watermarked image, while preserving the watermark. We empirically validate our method's increased robustness to these attacks. Taken together, our results suggest that content-aware watermarks can mitigate risks arising from image-generative models.
The rising use of deepfakes in criminal activities presents a significant issue, inciting widespread controversy. While numerous studies have tackled this problem, most primarily focus on deepfake detection. These reactive solutions are insufficient as a fundamental approach for crimes where authenticity is disregarded. Existing proactive defenses also have limitations, as they are effective only for deepfake models based on specific Generative Adversarial Networks (GANs), making them less applicable in light of recent advancements in diffusion-based models. In this paper, we propose a proactive defense method named FaceShield, which introduces novel defense strategies targeting deepfakes generated by Diffusion Models (DMs) and facilitates defenses on various existing GAN-based deepfake models through facial feature extractor manipulations. Our approach consists of three main components: (i) manipulating the attention mechanism of DMs to exclude protected facial features during the denoising process, (ii) targeting prominent facial feature extraction models to enhance the robustness of our adversarial perturbation, and (iii) employing Gaussian blur and low-pass filtering techniques to improve imperceptibility while enhancing robustness against JPEG compression. Experimental results on the CelebA-HQ and VGGFace2-HQ datasets demonstrate that our method achieves state-of-the-art performance against the latest deepfake models based on DMs, while also exhibiting transferability to GANs and showcasing greater imperceptibility of noise along with enhanced robustness.
Generating realistic and controllable weather effects in videos is valuable for many applications. Physics-based weather simulation requires precise reconstructions that are hard to scale to in-the-wild videos, while current video editing often lacks realism and control.In this work, we introduce WeatherWeaver, a video diffusion model that synthesizes diverse weather effects---including rain, snow, fog, and clouds---directly into any input video without the need for 3D modeling.Our model provides precise control over weather effect intensity and supports blending various weather types, ensuring both realism and adaptability.To overcome the scarcity of paired training data, we propose a novel data strategy combining synthetic videos, generative image editing, and auto-labeled real-world videos. Extensive evaluations show that our method outperforms state-of-the-art methodsin weather simulation and removal, providing high-quality, physically plausible, and scene-identity-preserving results over various real-world videos.
Diffusion models have emerged as the mainstream approach for visual generation. However, these models typically suffer from sample inefficiency and high training costs. Consequently, methods for efficient finetuning, inference and personalization were quickly adopted by the community. However, training these models in the first place still remains very costly. While several recent approaches--including masking, distillation, and architectural modifications--have been proposed to improve training efficiency, each of these methods comes with its own tradeoffs: some achieve enhanced performance at the expense of increased computational cost. In contrast, this work aims to improve training efficiency as well as generative performance at the same time through routes that act as transport mechanism for randomly selected tokens from early layers to deeper layers of the model. Our method is not limited to the common transformer-based model - it can also be applied to state-space models and achieves this without architectural modifications or additional parameters. Finally, we show that TREAD reduces the computational cost and simultaneously boosts model performance on the standard benchmark ImageNet-256 in class-conditional synthesis. Both of these benefits multiply to a convergence speedup of 14x at 400K training iterations compared to DiT and 37x compared to the best benchmark performance of DiT at 7M training iterations. Further, we achieve a competitive FID of 2.09 in a guided and 3.93 in an unguided setting which improves upon the DiT, without architectural changes. We will release our code.
Spike-based imaging, inspired by the human visual system, offers several advantages, including high temporal resolution and low power consumption, but suffers from significant image degradation in low-light conditions due to noise interference. Restoring spike images under such conditions poses a significant challenge, as traditional frame-based or spike-based techniques are ill-suited to handle such severe noise and unique noise characteristics. This paper proposes a novel approach for restoring low-light spike images using noise-modeled diffusion models. By establishing a noise-embedded spike imaging model under low light, we model the forward diffusion process as the degradation of spike images with proportional and residual terms and incorporate deterministic and non-deterministic components with reverse shifting, enabling the model to capture the distinctive spike noise structure. Additionally, we utilize region mask image, dark current map and spike density value as conditions to further guide the restoration process by providing prompts for degradation regions, deterministic parameters and noise intensity, respectively. Experimental results demonstrate that our method significantly outperforms existing spike-based reconstruction and diffusion-based image restoration methods in both quantitative performance and visual quality. The code and dataset are available at https://github.com/BIT-Vision/SpikeDiffusion.
Accurate surface reconstruction from unposed images is crucial for efficient 3D object or scene creation. However, it remains challenging, particularly for the joint camera pose estimation. Previous approaches have achieved impressive pose-free surface reconstruction results in dense-view settings, but could easily fail for sparse-view scenarios without sufficient visual overlap. In this paper, we propose a new technique for pose-free surface reconstruction, which follows triplane-based signed distance field (SDF) learning but regularizes the learning by explicit points sampled from ray-based diffusion of camera pose estimation. Our key contribution is a novel Geometric Consistent Ray Diffusion model (GCRayDiffusion), where we represent camera poses as neural bundle rays and regress the distribution of noisy rays via a diffusion model. More importantly, we further condition the denoising process of RGRayDiffusion using the triplane-based SDF of the entire scene, which provides effective 3D consistent regularization to achieve multi-view consistent camera pose estimation. Finally, we incorporate RGRayDiffusion into the triplane-based SDF learning by introducing on-surface geometric regularization from the sampling points of the neural bundle rays, which leads to highly accurate pose-free surface reconstruction results even for sparse-view inputs. Extensive evaluations on public datasets show that our GCRayDiffusion achieves more accurate camera pose estimation than previous approaches, with geometrically more consistent surface reconstruction results, especially given sparse-view inputs.
We introduce AV-Flow, an audio-visual generative model that animates photo-realistic 4D talking avatars given only text input. In contrast to prior work that assumes an existing speech signal, we synthesize speech and vision jointly. We demonstrate human-like speech synthesis, synchronized lip motion, lively facial expressions and head pose; all generated from just text characters. The core premise of our approach lies in the architecture of our two parallel diffusion transformers. Intermediate highway connections ensure communication between the audio and visual modalities, and thus, synchronized speech intonation and facial dynamics (e.g., eyebrow motion). Our model is trained with flow matching, leading to expressive results and fast inference. In case of dyadic conversations, AV-Flow produces an always-on avatar, that actively listens and reacts to the audio-visual input of a user. Through extensive experiments, we show that our method outperforms prior work, synthesizing natural-looking 4D talking avatars.
We present TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. By disentangling deterministic view transformations from stochastic content generation, our method achieves precise control over user-specified camera trajectories. We propose a novel dual-stream conditional video diffusion model that concurrently integrates point cloud renders and source videos as conditions, ensuring accurate view transformations and coherent 4D content generation. Instead of leveraging scarce multi-view videos, we curate a hybrid training dataset combining web-scale monocular videos with static multi-view datasets, by our innovative double-reprojection strategy, significantly fostering robust generalization across diverse scenes. Extensive evaluations on multi-view and large-scale monocular videos demonstrate the superior performance of our method. Code and pre-trained model will be released.
Personalizing text-to-image diffusion models is crucial for adapting the pre-trained models to specific target concepts, enabling diverse image generation. However, fine-tuning with few images introduces an inherent trade-off between aligning with the target distribution (e.g., subject fidelity) and preserving the broad knowledge of the original model (e.g., text editability). Existing sampling guidance methods, such as classifier-free guidance (CFG) and autoguidance (AG), fail to effectively guide the output toward well-balanced space: CFG restricts the adaptation to the target distribution, while AG compromises text alignment. To address these limitations, we propose personalization guidance, a simple yet effective method leveraging an unlearned weak model conditioned on a null text prompt. Moreover, our method dynamically controls the extent of unlearning in a weak model through weight interpolation between pre-trained and fine-tuned models during inference. Unlike existing guidance methods, which depend solely on guidance scales, our method explicitly steers the outputs toward a balanced latent space without additional computational overhead. Experimental results demonstrate that our proposed guidance can improve text alignment and target distribution fidelity, integrating seamlessly with various fine-tuning strategies.
Recent advances in text-to-image diffusion models have enabled the creation of a new form of digital art: optical illusions---visual tricks that create different perceptions of reality. However, adversaries may misuse such techniques to generate hateful illusions, which embed specific hate messages into harmless scenes and disseminate them across web communities. In this work, we take the first step toward investigating the risks of scalable hateful illusion generation and the potential for bypassing current content moderation models. Specifically, we generate 1,860 optical illusions using Stable Diffusion and ControlNet, conditioned on 62 hate messages. Of these, 1,571 are hateful illusions that successfully embed hate messages, either overtly or subtly, forming the Hateful Illusion dataset. Using this dataset, we evaluate the performance of six moderation classifiers and nine vision language models (VLMs) in identifying hateful illusions. Experimental results reveal significant vulnerabilities in existing moderation models: the detection accuracy falls below 0.245 for moderation classifiers and below 0.102 for VLMs. We further identify a critical limitation in their vision encoders, which mainly focus on surface-level image details while overlooking the secondary layer of information, i.e., hidden messages. To address this risk, we explore preliminary mitigation measures and identify the most effective approaches from the perspectives of image transformations and training-level strategies.
We present GaSLight, a method that generates spatially-varying lighting from regular images. Our method proposes using HDR Gaussian Splats as light source representation, marking the first time regular images can serve as light sources in a 3D renderer. Our two-stage process first enhances the dynamic range of images plausibly and accurately by leveraging the priors embedded in diffusion models. Next, we employ Gaussian Splats to model 3D lighting, achieving spatially variant lighting. Our approach yields state-of-the-art results on HDR estimations and their applications in illuminating virtual objects and scenes. To facilitate the benchmarking of images as light sources, we introduce a novel dataset of calibrated and unsaturated HDR captures to evaluate images as light sources. We assess our method using a combination of our dataset and an existing dataset from the literature. The code to reproduce our method is available at https://lvsn.github.io/gaslight/.
Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images
PDF ↗Volumetric scene reconstruction from a single image is crucial for a broad range of applications like autonomous driving and robotics. Recent volumetric reconstruction methods achieve impressive results, but generally require expensive 3D ground truth or multi-view supervision. We propose to leverage pre-trained 2D diffusion models and depth prediction models to generate synthetic scene geometry from a single image. This can then be used to distill a feed-forward scene reconstruction model. Our experiments on the challenging KITTI-360 and Waymo datasets demonstrate that our method matches or outperforms state-of-the-art baselines that use multi-view supervision, and offers unique advantages, for example regarding dynamic scenes.
Accurate and stable lane detection is crucial for the reliability of autonomous driving systems. A core challenge lies in predicting lane positions in complex scenarios, such as curved roads or when markings are ambiguous or absent.Conventional approaches leverage deep learning techniques to extract both high-level and low-level visual features, aiming to achieve a comprehensive understanding of the driving environment. However, these methods often rely on predefined anchors within a single-pass model, limiting their adaptability. The one-shot prediction paradigm struggles with precise lane estimation in challenging scenarios, such as curved roads or adverse conditions like low visibility at night.To address these limitations, we propose a novel cold diffusion-based framework that initializes lane predictions with predefined anchors and iteratively refines them. This approach retains the flexibility and progressive refinement capabilities of diffusion models while overcoming the constraints of traditional hot diffusion techniques.To further enhance the model's coarse-to-fine refinement capabilities, we introduce a multi-resolution image processing strategy, where images are analyzed at different timesteps to capture both global and local lane structure details. Besides, we incorporate a learnable noise variance schedule, enabling the model to dynamically adjust its learning process based on multi-resolution inputs.Experimental results demonstrate that our method significantly improves detection accuracy across a variety of challenging scenarios, outperforming state-of-the-art lane detection methods.
Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications. We introduce UnMix-NeRF, a framework that integrates spectral unmixing into NeRF, enabling joint hyperspectral novel view synthesis and unsupervised material segmentation. Our method models spectral reflectance via diffuse and specular components, where a learned dictionary of global endmembers represents pure material signatures, and per-point abundances capture their distribution. For material segmentation, we use spectral signature predictions along learned endmembers, allowing unsupervised material clustering. Additionally, UnMix-NeRF enables scene editing by modifying learned endmember dictionaries for flexible material-based appearance manipulation. Extensive experiments validate our approach, demonstrating superior spectral reconstruction and material segmentation to existing methods.
Environment-Agnostic Pose: Generating Environment-independent Object Representations for 6D Pose Estimation
PDF ↗This paper introduces EA6D, a novel diffusion-based framework for 6D pose estimation that operates effectively in any environment. Traditional pose estimation methods struggle with the variability and complexity of real-world scenarios, often leading to overfitting on controlled datasets and poor generalization to new scenes. To address these challenges, we propose a generative pose estimation paradigm that generates environment-independent object representations for pose estimation, which are robust to environmental variations such as illumination, occlusion, and background clutter. Specifically, we propose the novel Environment Decoupling Diffusion Model (EDDM) which separates object representations from environmental factors while enabling efficient few-step sampling by leveraging input image priors instead of pure noise initialization. We validate our approach on four standard benchmarks and a self-made dataset DiverseScenes. The results demonstrate that EA6D, trained using only synthetic data, can outperform the state-of-the-art methods with both synthetic and realistic data. In particular, for fair comparisons with synthetic data, we can exceed the previous SOTA by 18.1% and 33.5% on Linemod and Linemod-Occluded datasets respectively. Project page: https://github.com/acmff22/EA6D