Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmetric with actors and reactors in atomic interaction periods. In this paper we comprehensively analyze the asymmetric dynamic synchronous and detailed nature of human-human interactions and propose the first multi-setting human action-reaction synthesis benchmark to generate human reactions conditioned on given human actions. To begin with we propose to annotate the actor-reactor order of the interaction sequences for the NTU120 InterHuman and Chi3D datasets. Based on them a diffusion-based generative model with a Transformer decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner where the future states of actors are unavailable to reactors. Quantitative and qualitative results show that our method can generate instant and plausible human reactions compared to the baselines and can generalize to unseen actor motions and viewpoint changes.
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Existing diffusion-based video editing models have made gorgeous advances for editing attributes of a source video over time but struggle to manipulate the motion information while preserving the original protagonist's appearance and background. To address this we propose MotionEditor the first diffusion model for video motion editing. MotionEditor incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence. While ControlNet enables direct generation based on skeleton poses it encounters challenges when modifying the source motion in the inverted noise due to contradictory signals between the noise (source) and the condition (reference). Our adapter complements ControlNet by involving source content to transfer adapted control signals seamlessly. Further we build up a two-branch architecture (a reconstruction branch and an editing branch) with a high-fidelity attention injection mechanism facilitating branch interaction. This mechanism enables the editing branch to query the key and value from the reconstruction branch in a decoupled manner making the editing branch retain the original background and protagonist appearance. We also propose a skeleton alignment algorithm to address the discrepancies in pose size and position. Experiments demonstrate the promising motion editing ability of MotionEditor both qualitatively and quantitatively. To the best of our knowledge MotionEditor is the first to use diffusion models specifically for video motion editing considering the origin dynamic background and camera movement.
Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress state-of-the-art CoSODs could be easily affected by some adversarial perturbations leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g. concept) of the co-salient objects. In this paper we propose a novel robustness enhancement framework by first learning the concept of the co-salient objects based on the input group images and then leveraging this concept to purify adversarial perturbations which are subsequently fed to CoSODs for robustness enhancement. Specifically we propose CosalPure containing two modules i.e. group-image concept learning and concept-guided diffusion purification. For the first module we adopt a pre-trained text-to-image diffusion model to learn the concept of co-salient objects within group images where the learned concept is robust to adversarial examples. For the second module we map the adversarial image to the latent space and then perform diffusion generation by embedding the learned concept into the noise prediction function as an extra condition. Our method can effectively alleviate the influence of the SOTA adversarial attack containing different adversarial patterns including exposure and noise. The extensive results demonstrate that our method could enhance the robustness of CoSODs significantly.
Monocular depth estimation has experienced significant progress on terrestrial images in recent years thanks to deep learning advancements. But it remains inadequate for underwater scenes primarily due to data scarcity. Given the inherent challenges of light attenuation and backscatter in water acquiring clear underwater images or precise depth is notably difficult and costly. To mitigate this issue learning-based approaches often rely on synthetic data or turn to self- or unsupervised manners. Nonetheless their performance is often hindered by domain gap and looser constraints. In this paper we propose a novel pipeline for generating photorealistic underwater images using accurate terrestrial depth. This approach facilitates the supervised training of models for underwater depth estimation effectively reducing the performance disparity between terrestrial and underwater environments. Contrary to previous synthetic datasets that merely apply style transfer to terrestrial images without scene content change our approach uniquely creates vivid non-existent underwater scenes by leveraging terrestrial depth data through the innovative Stable Diffusion model. Specifically we introduce a specialized Depth2Underwater ControlNet trained on prepared \ Underwater Depth Text\ data triplets for this generation task. Our newly developed dataset Atlantis enables terrestrial depth estimation models to achieve considerable improvements on unseen underwater scenes surpassing their terrestrial pretrained counterparts both quantitatively and qualitatively. Moreover we further show its practical utility by applying the improved depth in underwater image enhancement and its smaller domain gap from the LLVM perspective. Code and dataset are publicly available at https://github.com/zkawfanx/Atlantis.
Estimating full-body human motion via sparse tracking signals from head-mounted displays and hand controllers in 3D scenes is crucial to applications in AR/VR. One of the biggest challenges to this task is the one-to-many mapping from sparse observations to dense full-body motions which endowed inherent ambiguities. To help resolve this ambiguous problem we introduce a new framework to combine rich contextual information provided by scenes to benefit full-body motion tracking from sparse observations. To estimate plausible human motions given sparse tracking signals and 3D scenes we develop \text S ^2Fusion a unified framework fusing \underline S cene and sparse \underline S ignals with a conditional dif\underline Fusion model. \text S ^2Fusion first extracts the spatial-temporal relations residing in the sparse signals via a periodic autoencoder and then produces time-alignment feature embedding as additional inputs. Subsequently by drawing initial noisy motion from a pre-trained prior \text S ^2Fusion utilizes conditional diffusion to fuse scene geometry and sparse tracking signals to generate full-body scene-aware motions. The sampling procedure of \text S ^2Fusion is further guided by a specially designed scene-penetration loss and phase-matching loss which effectively regularizes the motion of the lower body even in the absence of any tracking signals making the generated motion much more plausible and coherent. Extensive experimental results have demonstrated that our \text S ^2Fusion outperforms the state-of-the-art in terms of estimation quality and smoothness.
Recently 3D content creation from text prompts has demonstrated remarkable progress by utilizing 2D and 3D diffusion models. While 3D diffusion models ensure great multi-view consistency their ability to generate high-quality and diverse 3D assets is hindered by the limited 3D data. In contrast 2D diffusion models find a distillation approach that achieves excellent generalization and rich details without any 3D data. However 2D lifting methods suffer from inherent view-agnostic ambiguity thereby leading to serious multi-face Janus issues where text prompts fail to provide sufficient guidance to learn coherent 3D results. Instead of retraining a costly viewpoint-aware model we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement. In this paper we propose Sherpa3D a new text-to-3D framework that achieves high-fidelity generalizability and geometric consistency simultaneously. Specifically we design a pair of guiding strategies derived from the coarse 3D prior generated by the 3D diffusion model: a structural guidance for geometric fidelity and a semantic guidance for 3D coherence. Employing the two types of guidance the 2D diffusion model enriches the 3D content with diversified and high-quality results. Extensive experiments show the superiority of our Sherpa3D over the state-of-the-art text-to-3D methods in terms of quality and 3D consistency.
Despite diffusion models having shown powerful abilities to generate photorealistic images generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together leading to a notably increased complexity of text-to-video generation (T2V). In this work we propose HiGen a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives i.e. structure level and content level. At the structure level we decompose the T2V task into two steps including spatial reasoning and temporal reasoning using a unified denoiser. Specifically we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level we extract two subtle cues from the content of the input video that can express motion and appearance changes respectively. These two cues then guide the model's training for generating videos enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. We have released our source code and models.
We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-attention proposed in prior work (e.g. MVDream) leads to content copying between views. Therefore we explicitly constrain the cross-view attention based on epipolar geometry. To further enhance 3D consistency we utilize Pl ?ucker coordinates derived from camera rays and inject them as positional encoding. This enables SPAD to reason over spatial proximity in 3D well. Compared to concurrent works that can only generate views at fixed azimuth and elevation (e.g. MVDream SyncDreamer) SPAD offers full camera control and achieves state-of-the-art results in novel view synthesis on unseen objects from the Objaverse and Google Scanned Objects datasets. Finally we demonstrate that text-to-3D generation using SPAD prevents the multi-face Janus issue.
We propose RoHM an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train neural networks to directly regress motion in 3D or learn data-driven motion priors and combine them with optimization at test time. RoHM is a novel diffusion-based motion model that conditioned on noisy and occluded input data reconstructs complete plausible motions in consistent global coordinates. Given the complexity of the problem -- requiring one to address different tasks (denoising and infilling) in different solution spaces (local and global motion) -- we decompose it into two sub-tasks and learn two models one for global trajectory and one for local motion. To capture the correlations between the two we then introduce a novel conditioning module combining it with an iterative inference scheme. We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling. Extensive experiments on three popular datasets show that our method outperforms state-of-the-art approaches qualitatively and quantitatively while being faster at test time. The code is available at https://sanweiliti.github.io/ROHM/ROHM.html.
Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However these methods heavily rely on the outputs of existing models leading to error accumulation in geometry and appearance that prevent the models from being used in various scenarios (e.g. outdoor and unreal scenarios). To address this limitation we generatively refine the newly generated local views by querying and aggregating global 3D information and then progressively generate the 3D scene. Specifically we employ a tri-plane features-based NeRF as a unified representation of the 3D scene to constrain global 3D consistency and propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior from 2D diffusion model as well as the global 3D information of the current scene. Our extensive experiments demonstrate that in comparison to previous methods our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.
We present a novel theory that establishes the relationship between light transport in visible and thermal infrared and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the absorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on applying energy conservation principle at each pixel independently. We validate our theory using real-world experiments on diffuse objects made of different materials that exhibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.
Panorama video recently attracts more interest in both study and application courtesy of its immersive experience. Due to the expensive cost of capturing 360-degree panoramic videos generating desirable panorama videos by prompts is urgently required. Lately the emerging text-to-video (T2V) diffusion methods demonstrate notable effectiveness in standard video generation. However due to the significant gap in content and motion patterns between panoramic and standard videos these methods encounter challenges in yielding satisfactory 360-degree panoramic videos. In this paper we propose a pipeline named 360-Degree Video Diffusion model (360DVD) for generating 360-degree panoramic videos based on the given prompts and motion conditions. Specifically we introduce a lightweight 360-Adapter accompanied by 360 Enhancement Techniques to transform pre-trained T2V models for panorama video generation. We further propose a new panorama dataset named WEB360 consisting of panoramic video-text pairs for training 360DVD addressing the absence of captioned panoramic video datasets. Extensive experiments demonstrate the superiority and effectiveness of 360DVD for panorama video generation.
Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However many generated images still suffer from issues such as artifacts/implausibility misalignment with text descriptions and low aesthetic quality. Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text and (ii) annotating which words in the text prompt are misrepresented or missing on the image. We collect such rich human feedback on 18K generated images (RichHF-18K) and train a multimodal transformer to predict the rich feedback automatically. We show that the predicted rich human feedback can be leveraged to improve image generation for example by selecting high-quality training data to finetune and improve the generative models or by creating masks with predicted heatmaps to inpaint the problematic regions. Notably the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants). The RichHF-18K data set will be released in our GitHub repository: https://github.com/google-research/google-research/tree/master/richhf_18k.
Recovering ghost-free High Dynamic Range (HDR) images from multiple Low Dynamic Range (LDR) images becomes challenging when the LDR images exhibit saturation and significant motion. Recent Diffusion Models (DMs) have been introduced in HDR imaging field demonstrating promising performance particularly in achieving visually perceptible results compared to previous DNN-based methods. However DMs require extensive iterations with large models to estimate entire images resulting in inefficiency that hinders their practical application. To address this challenge we propose the Low-Frequency aware Diffusion (LF-Diff) model for ghost-free HDR imaging. The key idea of LF-Diff is implementing the DMs in a highly compacted latent space and integrating it into a regression-based model to enhance the details of reconstructed images. Specifically as low-frequency information is closely related to human visual perception we propose to utilize DMs to create compact low-frequency priors for the reconstruction process. In addition to take full advantage of the above low-frequency priors the Dynamic HDR Reconstruction Network (DHRNet) is carried out in a regression-based manner to obtain final HDR images. Extensive experiments conducted on synthetic and real-world benchmark datasets demonstrate that our LF-Diff performs favorably against several state-of-the-art methods and is 10x faster than previous DM-based methods.
Dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures.With empirical observations we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this we introduce Dataset Distillation via Disentangled Diffusion Model (D^4M) an efficient framework for dataset distillation. Compared to architecture-dependent methods D^4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments D^4M demonstrates superior performance and robust generalization surpassing the SOTA methods across most aspects.
Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with harmful stereotypes and misrepresentations of cultures. We improve inclusive representation in generated images by (1) engaging with communities to collect a culturally representative dataset that we call the Cross-Cultural Understanding Benchmark (CCUB) and (2) proposing a novel Self-Contrastive Fine-Tuning (SCoFT pronounced /soft/) method that leverages the model's known biases to self-improve. SCoFT is designed to prevent overfitting on small datasets encode only high-level information from the data and shift the generated distribution away from misrepresentations encoded in a pretrained model. Our user study conducted on 51 participants from 5 different countries based on their self-selected national cultural affiliation shows that fine-tuning on CCUB consistently generates images with higher cultural relevance and fewer stereotypes when compared to the Stable Diffusion baseline which is further improved with our SCoFT technique.
Sampling from the posterior distribution in latent diffusion models for inverse problems is computationally challenging. Existing methods often rely on Tweedie's first-order moments that tend to induce biased results. Second-order approximations are computationally prohibitive making standard reverse diffusion processes intractable for posterior sampling. This paper presents Second-order Tweedie sampler from Surrogate Loss (STSL) a novel sampler offering efficiency comparable to first-order Tweedie while enabling tractable reverse processes using second-order approximation. Theoretical results reveal that our approach utilizing for the trace of the Hessian with only O(1) compute establishes a lower bound through a surrogate loss and enables a tractable reverse process. We show STSL outperforms SoTA solvers PSLD and P2L by reducing neural function evaluations by 4X and 8X respectively while enhancing sampling quality on FFHQ ImageNet and COCO benchmarks. Moreover STSL extends to text guided image editing and mitigates residual distortions in corrupted images. To our best knowledge this is the first work to offer an efficient second order approximation for solving inverse problems using latent diffusion and editing real world images with corruptions.
Pretrained diffusion models and their outputs are widely accessible due to their exceptional capacity for synthesizing high-quality images and their open-source nature. The users however may face litigation risks owing to the models' tendency to memorize and regurgitate training data during inference. To address this we introduce Anti-Memorization Guidance (AMG) a novel framework employing three targeted guidance strategies for the main causes of memorization: image and caption duplication and highly specific user prompts. Consequently AMG ensures memorization-free outputs while maintaining high image quality and text alignment leveraging the synergy of its guidance methods each indispensable in its own right. AMG also features an innovative automatic detection system for potential memorization during each step of inference process allows selective application of guidance strategies minimally interfering with the original sampling process to preserve output utility. We applied AMG to pretrained Denoising Diffusion Probabilistic Models (DDPM) and Stable Diffusion across various generation tasks. The results demonstrate that AMG is the first approach to successfully eradicates all instances of memorization with no or marginal impacts on image quality and text-alignment as evidenced by FID and CLIP scores.
We concentrate on a novel human-centric image synthesis task that is given only one reference facial photograph it is expected to generate specific individual images with diverse head positions poses facial expressions and illuminations in different contexts. To accomplish this goal we argue that our generative model should be capable of the following favorable characteristics: (1) a strong visual and semantic understanding of our world and human society for basic object and human image generation. (2) generalizable identity preservation ability. (3) flexible and fine-grained head control. Recently large pre-trained text-to-image diffusion models have shown remarkable results serving as a powerful generative foundation. As a basis we aim to unleash the above two capabilities of the pre-trained model. In this work we present a new framework named CapHuman. We embrace the "encode then learn to align" paradigm which enables generalizable identity preservation for new individuals without cumbersome tuning at inference. CapHuman encodes identity features and then learns to align them into the latent space. Moreover we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner. Extensive qualitative and quantitative analyses demonstrate our CapHuman can produce well-identity-preserved photo-realistic and high-fidelity portraits with content-rich representations and various head renditions superior to established baselines. Code and checkpoint will be released at https://github.com/VamosC/CapHuman.
The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to optimize a 3D representation such that the rendered image maintains a high likelihood as evaluated by a pre-trained diffusion model. However this distillation process involves finding a correct mode in the high-dimensional and large-variance distribution produced by the diffusion model. This task is challenging and often leads to issues such as over-saturation over-smoothing and Janus-like artifacts in the 3D generation. In this paper we propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Instead of focusing on mode-seeking our method directly models the distribution discrepancy between multi-view renderings and diffusion priors in an adversarial manner which unlocks the generation of high-fidelity and photorealistic 3D content conditioned on a single image and prompt. Moreover by harnessing the latent space of GANs and expressive diffusion model priors our method enables a wide variety of 3D applications including single-view reconstruction high diversity generation and continuous 3D interpolation in open domain. Our experiments demonstrate the superiority of our pipeline compared to previous works in terms of generation quality and diversity.