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Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu 等

Current perceptive models heavily depend on resource-intensive datasets prompting the need for innovative solutions. Leveraging recent advances in diffusion models synthetic data by constructing image inputs from various annotations proves beneficial for downstream tasks. While prior methods have separately addressed generative and perceptive models DetDiffusion for the first time harmonizes both tackling the challenges in generating effective data for perceptive models. To enhance image generation with perceptive models we introduce perception-aware loss (P.A. loss) through segmentation improving both quality and controllability. To boost the performance of specific perceptive models our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation. Experimental results from the object detection task highlight DetDiffusion's superior performance establishing a new state-of-the-art in layout-guided generation. Furthermore image syntheses from DetDiffusion can effectively augment training data significantly enhancing downstream detection performance.

Jiapeng Tang, Angela Dai, Yinyu Nie, Lev Markhasin, Justus Thies, Matthias Nießner

We introduce Diffusion Parametric Head Models (DPHMs) a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences. While recent volumetric head models such as NPHMs can now excel in representing high-fidelity head geometries tracking and reconstructing heads from real-world single-view depth sequences remains very challenging as the fitting to partial and noisy observations is underconstrained. To tackle these challenges we propose a latent diffusion-based prior to regularize volumetric head reconstruction and tracking. This prior-based regularizer effectively constrains the identity and expression codes to lie on the underlying latent manifold which represents plausible head shapes. To evaluate the effectiveness of the diffusion-based prior we collect a dataset of monocular Kinect sequences consisting of various complex facial expression motions and rapid transitions. We compare our method to state-of-the-art tracking methods and demonstrate improved head identity reconstruction as well as robust expression tracking.

Wentao Qu, Yuantian Shao, Lingwu Meng, Xiaoshui Huang, Liang Xiao

Point cloud upsampling (PCU) enriches the representation of raw point clouds significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design. In a different way we dive deeper into directly modelling the gradient of data distribution from dense point clouds. In this paper we proposed a conditional denoising diffusion probabilistic model (DDPM) for point cloud upsampling called PUDM. Specifically PUDM treats the sparse point cloud as a condition and iteratively learns the transformation relationship between the dense point cloud and the noise. Simultaneously PUDM aligns with a dual mapping paradigm to further improve the discernment of point features. In this context PUDM enables learning complex geometry details in the ground truth through the dominant features while avoiding an additional upsampling module design. Furthermore to generate high-quality arbitrary-scale point clouds during inference PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover PUDM exhibits strong noise robustness in experimental results. In the quantitative and qualitative evaluations on PU1K and PUGAN PUDM significantly outperformed existing methods in terms of Chamfer Distance (CD) and Hausdorff Distance (HD) achieving state of the art (SOTA) performance.

Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, Siyu Tang

We propose Diffusion Noise Optimization (DNO) a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks. Instead of training a task-specific diffusion model for each new task DNO operates by optimizing the diffusion latent noise of an existing pre-trained text-to-motion model. Given the corresponding latent noise of a human motion it propagates the gradient from the target criteria defined on the motion space through the whole denoising process to update the diffusion latent noise. As a result DNO supports any use cases where criteria can be defined as a function of motion. In particular we show that for motion editing and control DNO outperforms existing methods in both achieving the objective and preserving the motion content. DNO accommodates a diverse range of editing modes including changing trajectory pose joint locations or avoiding newly added obstacles. In addition DNO is effective in motion denoising and completion producing smooth and realistic motion from noisy and partial inputs. DNO achieves these results at inference time without the need for model retraining offering great versatility for any defined reward or loss function on the motion representation.

Ryan Po, Guandao Yang, Kfir Aberman, Gordon Wetzstein

Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited pre-defined set of them they fall short of achieving scalability where a single model can seamlessly render countless concepts. In this paper we address a new problem called Modular Customization with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts. This allows the merged model to jointly synthesize concepts in one image without compromising fidelity or incurring any additional computational costs. To address this problem we introduce Orthogonal Adaptation a method designed to encourage the customized models which do not have access to each other during fine-tuning to have orthogonal residual weights. This ensures that during inference time the customized models can be summed with minimal interference. Our proposed method is both simple and versatile applicable to nearly all optimizable weights in the model architecture. Through an extensive set of quantitative and qualitative evaluations our method consistently outperforms relevant baselines in terms of efficiency and identity preservation demonstrating a significant leap toward scalable customization of diffusion models.

Ying-Tian Liu, Yuan-Chen Guo, Guan Luo, Heyi Sun, Wei Yin, Song-Hai Zhang

Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper we present PI3D a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin.

Hsin-Ying Lee, Hung-Yu Tseng, Hsin-Ying Lee, Ming-Hsuan Yang

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. To address the misalignment between deterministic prediction tasks and stochastic T2I models we reformulate the diffusion process through a sequence of interpolations establishing a deterministic mapping between input RGB images and output prediction distributions. To preserve generalizability we use low-rank adaptation to fine-tune pre-trained models. Extensive experiments across five tasks including 3D property estimation semantic segmentation and intrinsic image decomposition showcase the efficacy of the proposed method. Despite limited-domain training data the approach yields faithful estimations for arbitrary images surpassing existing state-of-the-art algorithms.

Chengfeng Zhao, Juze Zhang, Jiashen Du, Ziwei Shan, Junye Wang, Jingyi Yu, Jingya Wang, Lan Xu

We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper we present I'm-HOI a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter we tailor a category-aware motion diffusion model which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body hand and object motions. Moreover we contribute a large dataset with ground truth human and object motions dense RGB inputs and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community.

Haipeng Liu, Yang Wang, Biao Qian, Meng Wang, Yong Rui

Denoising diffusion probabilistic models (DDPMs) for image inpainting aim to add the noise to the texture of the image during the forward process and recover the masked regions with the unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics generation the existing arts suffer from the semantic discrepancy between the masked and unmasked regions since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process leading to the large discrepancy between them. In this paper we aim to answer how the unmasked semantics guide the texture denoising process; together with how to tackle the semantic discrepancy to facilitate the consistent and meaningful semantics generation. To this end we propose a novel structure-guided diffusion model for image inpainting named StrDiffusion to reformulate the conventional texture denoising process under the structure guidance to derive a simplified denoising objective for image inpainting while revealing: 1) the semantically sparse structure is beneficial to tackle the semantic discrepancy in the early stage while the dense texture generates the reasonable semantics in the late stage; 2) the semantics from the unmasked regions essentially offer the time-dependent structure guidance for the texture denoising process benefiting from the time-dependent sparsity of the structure semantics. For the denoising process a structure-guided neural network is trained to estimate the simplified denoising objective by exploiting the consistency of the denoised structure between masked and unmasked regions. Besides we devise an adaptive resampling strategy as a formal criterion as whether the structure is competent to guide the texture denoising process while regulate their semantic correlations. Extensive experiments validate the merits of StrDiffusion over the state-of-the-arts. Our code is available at https://github.com/htyjers/StrDiffusion.

Yanhui Wang, Jianmin Bao, Wenming Weng, Ruoyu Feng, Dacheng Yin, Tao Yang, Jingxu Zhang, Qi Dai, Zhiyuan Zhao, Chunyu Wang 等

We present MicroCinema a straightforward yet effective framework for high-quality and coherent text-to-video generation. Unlike existing approaches that align text prompts with video directly MicroCinema introduces a Divide-and-Conquer strategy which divides the text-to-video into a two-stage process: text-to-image generation and image&text-to-video generation. This strategy offers two significant advantages. a) It allows us to take full advantage of the recent advances in text-to-image models such as Stable Diffusion Midjourney and DALLE to generate photorealistic and highly detailed images. b) Leveraging the generated image the model can allocate less focus to fine-grained appearance details prioritizing the efficient learning of motion dynamics. To implement this strategy effectively we introduce two core designs. First we propose the Appearance Injection Network enhancing the preservation of the appearance of the given image. Second we introduce the Appearance Noise Prior a novel mechanism aimed at maintaining the capabilities of pre-trained 2D diffusion models. These design elements empower MicroCinema to generate high-quality videos with precise motion guided by the provided text prompts. Extensive experiments demonstrate the superiority of the proposed framework. Concretely MicroCinema achieves SOTA zero-shot FVD of 342.86 on UCF-101 and 377.40 on MSR-VTT.

Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song

This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos. This proficiency is underpinned by their robust cross-modal capabilities and shape bias findings that are substantiated through our pilot studies. In order to harness pre-trained diffusion models effectively we introduce a straightforward yet powerful strategy focused on two key aspects: selecting optimal feature layers and utilising visual and textual prompts. For the former we identify which layers are most enriched with information and are best suited for the specific retrieval requirements (category-level or fine-grained). Then we employ visual and textual prompts to guide the model's feature extraction process enabling it to generate more discriminative and contextually relevant cross-modal representations. Extensive experiments on several benchmark datasets validate significant performance improvements.

Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman, Taesung Park

Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality. We enforce the one-step image generator match the diffusion model at distribution level by minimizing an approximate KL divergence whose gradient can be expressed as the difference between 2 score functions one of the target distribution and the other of the synthetic distribution being produced by our one-step generator. The score functions are parameterized as two diffusion models trained separately on each distribution. Combined with a simple regression loss matching the large-scale structure of the multi-step diffusion outputs our method outperforms all published few-step diffusion approaches reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot COCO-30k comparable to Stable Diffusion but orders of magnitude faster. Utilizing FP16 inference our model can generate images at 20 FPS on modern hardware.

Evonne Ng, Javier Romero, Timur Bagautdinov, Shaojie Bai, Trevor Darrell, Angjoo Kanazawa, Alexander Richard

We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction. Given speech audio we output multiple possibilities of gestural motion for an individual including face body and hands. The key behind our method is in combining the benefits of sample diversity from vector quantization with the high-frequency details obtained through diffusion to generate more dynamic expressive motion. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures (e.g. sneers and smirks). To facilitate this line of research we introduce a first-of-its-kind multi-view conversational dataset that allows for photorealistic reconstruction. Experiments show our model generates appropriate and diverse gestures outperforming both diffusion- and VQ-only methods. Furthermore our perceptual evaluation highlights the importance of photorealism (vs. meshes) in accurately assessing subtle motion details in conversational gestures. Code and dataset available on project page.

Muhammad Hamza Mughal, Rishabh Dabral, Ikhsanul Habibie, Lucia Donatelli, Marc Habermann, Christian Theobalt

Gestures play a key role in human communication. Recent methods for co-speech gesture generation while managing to generate beat-aligned motions struggle generating gestures that are semantically aligned with the utterance. Compared to beat gestures that align naturally to the audio signal semantically coherent gestures require modeling the complex interactions between the language and human motion and can be controlled by focusing on certain words. Therefore we present ConvoFusion a diffusion-based approach for multi-modal gesture synthesis which can not only generate gestures based on multi-modal speech inputs but can also facilitate controllability in gesture synthesis. Our method proposes two guidance objectives that allow the users to modulate the impact of different conditioning modalities (e.g. audio vs text) as well as to choose certain words to be emphasized during gesturing. Our method is versatile in that it can be trained either for generating monologue gestures or even the conversational gestures. To further advance the research on multi-party interactive gestures the DnD Group Gesture dataset is released which contains 6 hours of gesture data showing 5 people interacting with one another. We compare our method with several recent works and demonstrate effectiveness of our method on a variety of tasks. We urge the reader to watch our supplementary video at https://vcai.mpi-inf.mpg.de/projects/ConvoFusion/.

Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, Cheng-Hao Kuo

Machine learning models face generalization challenges when exposed to out-of-distribution (OOD) samples with unforeseen distribution shifts. Recent research reveals that for vision tasks test-time adaptation employing diffusion models can achieve state-of-the-art accuracy improvements on OOD samples by generating domain-aligned samples without altering the model's weights. Unfortunately those studies have primarily focused on pixel-level corruptions thereby lacking the generalization to adapt to a broader range of OOD types. We introduce Generalized Diffusion Adaptation (GDA) a novel diffusion-based test-time adaptation method robust against diverse OOD types. Specifically GDA iteratively guides the diffusion by applying a marginal entropy loss derived from the model in conjunction with style and content preservation losses during the reverse sampling process. In other words GDA considers the model's output behavior and the samples' semantic information as a whole reducing ambiguity in downstream tasks. based adaptation. Evaluation across various model architectures and OOD benchmarks indicates that GDA consistently surpasses previous diffusion-based adaptation methods. Notably it achieves the highest classification accuracy improvements ranging from 4.4% to 5.02% on ImageNet-C and 2.5% to 7.4% on Rendition Sketch and Stylized benchmarks. This performance highlights GDA's generalization to a broader range of OOD benchmarks.

Cheng Chen, Xiaofeng Yang, Fan Yang, Chengzeng Feng, Zhoujie Fu, Chuan-Sheng Foo, Guosheng Lin, Fayao Liu

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g. faces on the back view) and inaccurate shapes (e.g. animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover to ensure accurate appearances of different views we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity.

Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, He Zhang, Wei Xiong, Daniel Aliaga

Generative object compositing emerges as a promising new avenue for compositional image editing. However the requirement of object identity preservation poses a significant challenge limiting practical usage of most existing methods. In response this paper introduces IMPRINT a novel diffusion-based generative model trained with a two-stage learning framework that decouples learning of identity preservation from that of compositing. The first stage is targeted for context-agnostic identity-preserving pretraining of the object encoder enabling the encoder to learn an embedding that is both view-invariant and conducive to enhanced detail preservation. The subsequent stage leverages this representation to learn seamless harmonization of the object composited to the background. In addition IMPRINT incorporates a shape-guidance mechanism offering user-directed control over the compositing process. Extensive experiments demonstrate that IMPRINT significantly outperforms existing methods and various baselines on identity preservation and composition quality.

Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, Wei-Shi Zheng

Universal image restoration is a practical and potential computer vision task for real-world applications. The main challenge of this task is handling the different degradation distributions at once. Existing methods mainly utilize task-specific conditions (e.g. prompt) to guide the model to learn different distributions separately named multi-partite mapping. However it is not suitable for universal model learning as it ignores the shared information between different tasks. In this work we propose an advanced selective hourglass mapping strategy based on diffusion model termed DiffUIR. Two novel considerations make our DiffUIR non-trivial. Firstly we equip the model with strong condition guidance to obtain accurate generation direction of diffusion model (selective). More importantly DiffUIR integrates a flexible shared distribution term (SDT) into the diffusion algorithm elegantly and naturally which gradually maps different distributions into a shared one. In the reverse process combined with SDT and strong condition guidance DiffUIR iteratively guides the shared distribution to the task-specific distribution with high image quality (hourglass). Without bells and whistles by only modifying the mapping strategy we achieve state-of-the-art performance on five image restoration tasks 22 benchmarks in the universal setting and zero-shot generalization setting. Surprisingly by only using a lightweight model (only 0.89M) we could achieve outstanding performance. The source code and pre-trained models are available at https://github.com/iSEE-Laboratory/DiffUIR

Yanyu Li, Xian Liu, Anil Kag, Ju Hu, Yerlan Idelbayev, Dhritiman Sagar, Yanzhi Wang, Sergey Tulyakov, Jian Ren

Diffusion-based text-to-image generative models e.g. Stable Diffusion have revolutionized the field of content generation enabling significant advancements in areas like image editing and video synthesis. Despite their formidable capabilities these models are not without their limitations. It is still challenging to synthesize an image that aligns well with the input text and multiple runs with carefully crafted prompts are required to achieve satisfactory results. To mitigate these limitations numerous studies have endeavored to fine-tune the pre-trained diffusion models i.e.. UNet utilizing various technologies. Yet amidst these efforts a pivotal question of text-to-image diffusion model training has remained largely unexplored: Is it possible and feasible to fine-tune the text encoder to improve the performance of text-to-image diffusion models? Our findings reveal that instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models we can enhance it through our proposed fine-tuning approach TextCraftor leading to substantial improvements in quantitative benchmarks and human assessments. Interestingly our technique also empowers controllable image generation through the interpolation of different text encoders fine-tuned with various rewards. We also demonstrate that TextCraftor is orthogonal to UNet finetuning and can be combined to further improve generative quality.

Zirui Wang, Zhizhou Sha, Zheng Ding, Yilin Wang, Zhuowen Tu

We present TokenCompose a Latent Diffusion Model for text-to-image generation that achieves enhanced consistency between user-specified text prompts and model-generated images. Despite its tremendous success the standard denoising process in the Latent Diffusion Model takes text prompts as conditions only absent explicit constraint for the consistency between the text prompts and the image contents leading to unsatisfactory results for composing multiple object categories. Our proposed TokenCompose aims to improve multi-category instance composition by introducing the token-wise consistency terms between the image content and object segmentation maps in the finetuning stage. TokenCompose can be applied directly to the existing training pipeline of text-conditioned diffusion models without extra human labeling information. By finetuning Stable Diffusion with our approach the model exhibits significant improvements in multi-category instance composition and enhanced photorealism for its generated images.