Despite significant progress in generative image synthesis and full-body generation in particular, state-of-the-art methods are either context-independent, overly reliant to text prompts, or bound to the curated training datasets, such as fashion images with monotonous backgrounds. Here, our goal is to generate people in clothing that is semantically appropriate for a given scene. To this end, we present ESP, a novel method for context-aware full-body generation, that enables photo-realistic inpainting of people into existing "in-the-wild" photographs. ESP is conditioned on a 2D pose and contextual cues that are extracted from the environment photograph and integrated into the generation process. Our models are trained on a dataset containing a set of in-the-wild photographs of people covering a wide range of different environments. The method is analyzed quantitatively and qualitatively, and we show that ESP outperforms state-of-the-art on the task of contextual full-body generation.
With the help of Score Distillation Sampling(SDS) and the rapid development of various trainable 3D representations, Text-to-Image(T2I) diffusion models have been applied to 3D generation tasks and achieved considerable results. There are also some attempts toward the task of editing 3D objects leveraging this Text-to-3D pipeline. However, most methods currently focus on adding additional geometries, overwriting textures or both. But few of them can perform non-rigid transformation of 3D objects. For those who can perform non-rigid editing, on the other hand, suffer from low-resolution, lack of fidelity and poor flexibility. In order to address these issues, we present: Plasticine3D, a general, high-fidelity, photo-realistic and controllable non-rigid editing pipeline. Firstly, our work divides the editing process into a geometry editing stage and a texture editing stage to achieve more detailed and photo-realistic results ; Secondly, in order to perform non-rigid transformation with controllable results while maintain the fidelity towards original 3D models in the same time, we propose a multi-view-embedding(MVE) optimization strategy to ensure that the diffusion model learns the overall features of the original object and an embedding-fusion(EF) to control the degree of editing by adjusting the value of the fusing rate. We also design a geometry processing step before optimizing on the base geometry to cope with different needs of various editing tasks. Further more, to fully leverage the geometric prior from the original 3D object, we provide an optional replacement of score distillation sampling named score projection sampling(SPS) which enables us to directly perform optimization from the origin 3D mesh in most common median non-rigid editing scenarios. We demonstrate the effectiveness of our method on both the non-rigid 3D editing task and general 3D editing task.
Stable Diffusion web UI (SD-WebUI) is a comprehensive project that provides a browser interface based on Gradio library for Stable Diffusion models. In this paper, We propose a novel WebUI plugin called EasyPhoto, which enables the generation of AI portraits. By training a digital doppelganger of a specific user ID using 5 to 20 relevant images, the finetuned model (according to the trained LoRA model) allows for the generation of AI photos using arbitrary templates. Our current implementation supports the modification of multiple persons and different photo styles. Furthermore, we allow users to generate fantastic template image with the strong SDXL model, enhancing EasyPhoto's capabilities to deliver more diverse and satisfactory results. The source code for EasyPhoto is available at: https://github.com/aigc-apps/sd-webui-EasyPhoto. We also support a webui-free version by using diffusers: https://github.com/aigc-apps/EasyPhoto. We are continuously enhancing our efforts to expand the EasyPhoto pipeline, making it suitable for any identification (not limited to just the face), and we enthusiastically welcome any intriguing ideas or suggestions.
Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax, diffuse and specular materials, scene motion, and illuminant variation. While decomposing all of these effects from a stack of misaligned images is a highly ill-conditioned task, the conventional align-and-merge burst pipeline takes the other extreme: blending them into a single image. In this work, we propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields -- networks trained to map input coordinates to spline control points. Our method is able to, during test-time optimization, jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers. Then, by discarding the obstruction layer, we can perform a range of tasks including seeing through occlusions, reflection suppression, and shadow removal. Validated on complex synthetic and in-the-wild captures we find that, with no post-processing steps or learned priors, our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches.
We present 3D Points Splatting Hand Reconstruction (3D-PSHR), a real-time and photo-realistic hand reconstruction approach. We propose a self-adaptive canonical points upsampling strategy to achieve high-resolution hand geometry representation. This is followed by a self-adaptive deformation that deforms the hand from the canonical space to the target pose, adapting to the dynamic changing of canonical points which, in contrast to the common practice of subdividing the MANO model, offers greater flexibility and results in improved geometry fitting. To model texture, we disentangle the appearance color into the intrinsic albedo and pose-aware shading, which are learned through a Context-Attention module. Moreover, our approach allows the geometric and the appearance models to be trained simultaneously in an end-to-end manner. We demonstrate that our method is capable of producing animatable, photorealistic and relightable hand reconstructions using multiple datasets, including monocular videos captured with handheld smartphones and large-scale multi-view videos featuring various hand poses. We also demonstrate that our approach achieves real-time rendering speeds while simultaneously maintaining superior performance compared to existing state-of-the-art methods.
Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. Based on the observation from the influence of the cross-attention map on object placement in generated images, we propose a localized attention constraint into the cross-attention layer to address the unreasonable positioning problem of scene text. Additionally, we introduce contrastive image-level prompts to further refine the position of the textual region and achieve more accurate scene text generation. Experiments demonstrate that our method outperforms the existing method in both the accuracy of text recognition and the naturalness of foreground-background blending.
Segment Anything Model (SAM) is a foundation model for interactive segmentation, and it has catalyzed major advances in generative AI, computational photography, and medical imaging. This model takes in an arbitrary user input and provides segmentation masks of the corresponding objects. It is our goal to develop a version of SAM that is appropriate for use in a photography app. The original SAM model has a few challenges in this setting. First, original SAM a 600 million parameter based on ViT-H, and its high computational cost and large model size that are not suitable for todays mobile hardware. We address this by proposing the SqueezeSAM model architecture, which is 50x faster and 100x smaller than SAM. Next, when a user takes a photo on their phone, it might not occur to them to click on the image and get a mask. Our solution is to use salient object detection to generate the first few clicks. This produces an initial segmentation mask that the user can interactively edit. Finally, when a user clicks on an object, they typically expect all related pieces of the object to be segmented. For instance, if a user clicks on a person t-shirt in a photo, they expect the whole person to be segmented, but SAM typically segments just the t-shirt. We address this with a new data augmentation scheme, and the end result is that if the user clicks on a person holding a basketball, the person and the basketball are all segmented together.
We present DiffPortrait3D, a conditional diffusion model that is capable of synthesizing 3D-consistent photo-realistic novel views from as few as a single in-the-wild portrait. Specifically, given a single RGB input, we aim to synthesize plausible but consistent facial details rendered from novel camera views with retained both identity and facial expression. In lieu of time-consuming optimization and fine-tuning, our zero-shot method generalizes well to arbitrary face portraits with unposed camera views, extreme facial expressions, and diverse artistic depictions. At its core, we leverage the generative prior of 2D diffusion models pre-trained on large-scale image datasets as our rendering backbone, while the denoising is guided with disentangled attentive control of appearance and camera pose. To achieve this, we first inject the appearance context from the reference image into the self-attention layers of the frozen UNets. The rendering view is then manipulated with a novel conditional control module that interprets the camera pose by watching a condition image of a crossed subject from the same view. Furthermore, we insert a trainable cross-view attention module to enhance view consistency, which is further strengthened with a novel 3D-aware noise generation process during inference. We demonstrate state-of-the-art results both qualitatively and quantitatively on our challenging in-the-wild and multi-view benchmarks.
Recent advances in neural radiance fields enable novel view synthesis of photo-realistic images in dynamic settings, which can be applied to scenarios with human animation. Commonly used implicit backbones to establish accurate models, however, require many input views and additional annotations such as human masks, UV maps and depth maps. In this work, we propose ParDy-Human (Parameterized Dynamic Human Avatar), a fully explicit approach to construct a digital avatar from as little as a single monocular sequence. ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar. Our method is composed of two parts: A first module that deforms canonical 3D Gaussians according to SMPL vertices and a consecutive module that further takes their designed joint encodings and predicts per Gaussian deformations to deal with dynamics beyond SMPL vertex deformations. Images are then synthesized by a rasterizer. ParDy-Human constitutes an explicit model for realistic dynamic human avatars which requires significantly fewer training views and images. Our avatars learning is free of additional annotations such as masks and can be trained with variable backgrounds while inferring full-resolution images efficiently even on consumer hardware. We provide experimental evidence to show that ParDy-Human outperforms state-of-the-art methods on ZJU-MoCap and THUman4.0 datasets both quantitatively and visually.
Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of multiple years. Addressing this diagnostic odyssey thus have substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. However, existing methods using frontal facial photo were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates the facial images, demographic information (age, sex, ethnicity), and clinical notes of patients to improve prediction accuracy. Furthermore, we also introduce GestaltGPT, a GPT-based methodology with few-short learning capacities that exclusively harnesses textual inputs using a range of large language models (LLMs) including Llama 2, GPT-J and Falcon. We evaluated these methods on a diverse range of datasets, including 449 diseases from the GestaltMatcher Database, several in-house datasets on Beckwith-Wiedemann syndrome, Sotos syndrome, NAA10-related syndrome (neurodevelopmental syndrome) and others. Our results suggest that GestaltMML/GestaltGPT effectively incorporate multiple modalities of data, greatly narrow down candidate genetic diagnosis of rare diseases, and may facilitate the reinterpretation of genome/exome sequencing data.