DreamEditor: Text-Driven 3D Scene Editing with Neural Fields

Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we propose DreamEditor, a novel framework that enables users to perform controlled editing of neural fields using text prompts. By representing scenes as mesh-based neural fields, DreamEditor allows localized editing within specific regions. DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through score distillation sampling [29]. Extensive experiments have demonstrated that DreamEditor can accurately edit neural fields of real-world scenes according to the given text prompts while ensuring consistency in irrelevant areas. DreamEditor generates highly realistic textures and geometry, significantly surpassing previous works in both quantitative and qualitative evaluations

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects. Previous studies have used two-stage approaches that rely on pretrained NeRFs as real data to train diffusion models. In contrast, we propose a new single-stage training paradigm with an end-to-end objective that jointly optimizes a NeRF auto-decoder and a latent diffusion model, enabling simultaneous 3D reconstruction and prior learning, even from sparsely available views. At test time, we can directly sample the diffusion prior for unconditional generation, or combine it with arbitrary observations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates robust results comparable to or better than leading task-specific methods in unconditional generation and single/sparse-view 3D reconstruction.Comment: Project page: https://lakonik.github.io/ssdner

Learning Controllable 3D Diffusion Models from Single-view Images

Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulties in acquiring 3D ground truth data for training. On the other hand, 3D GANs that integrate implicit 3D representations into GANs have shown remarkable 3D-aware generation when trained only on single-view image datasets. However, 3D GANs do not provide straightforward ways to precisely control image synthesis. To address these challenges, We present Control3Diff, a 3D diffusion model that combines the strengths of diffusion models and 3D GANs for versatile, controllable 3D-aware image synthesis for single-view datasets. Control3Diff explicitly models the underlying latent distribution (optionally conditioned on external inputs), thus enabling direct control during the diffusion process. Moreover, our approach is general and applicable to any type of controlling input, allowing us to train it with the same diffusion objective without any auxiliary supervision. We validate the efficacy of Control3Diff on standard image generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various conditioning inputs such as images, sketches, and text prompts. Please see the project website (\url{https://jiataogu.me/control3diff}) for video comparisons.Comment: work in progres

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person from a sparse set of cameras, and further animate them with the user's pose control. While existing methods can either generalize to new persons or synthesize animations with user control, none of them can achieve both at the same time. We attribute this accomplishment to the employment of a 3D proxy for a shared multi-person human model, and further the warping of the spaces of different poses to a shared canonical pose space, in which we learn a neural field and predict the person- and pose-dependent deformations, as well as appearance with the features extracted from input images. To cope with the complexity of the large variations in body shapes, poses, and clothing deformations, we design our neural human model with disentangled geometry and appearance. Furthermore, we utilize the image features both at the spatial point and on the surface points of the 3D proxy for predicting person- and pose-dependent properties. Experiments show that our method significantly outperforms the state-of-the-arts on both tasks. The video and code are available at https://talegqz.github.io/neural_novel_actor

OR-019 VEGF-B inhibits skeletal muscle apoptosis after exercise in Chronic heart failure rats

Objective To investigate the effects of vascular endothelial growth factor B in aerobic exercise mediated chronic heart failure rats cardiac function improvement and skeletal muscle remodeling. Methods We employed transverse abdominal aortic constriction (TAC) inducing CHF in Sprague Dawley rats. Controls were sham-operated animals. At 4 weeks after surgery, rats were randomized to 4 weeks of aerobic exercise (CHF+E) or to untrained groups (CHF). After 8 weeks, all rats went echocardiography test. After which, rats were sacrificed and samples were collected. Muscular cytokine VEGFB and its receptor NRP1 expression were analyzed. Expression of apoptosis and muscle atrophy markers were assessed in cardiac muscle、gastrocnemius. Results TAC rats developed CHF (preserved LV ejection fraction, hypertrophy of myocardial cells, decreased FS, increased LVAW d and LVID s). Exercise ameliorate CHF rat cardiac function. TAC rat skeletal muscle developed irregular muscle fiber distribution.The two atrophy-related ubiquitin ligases atrogin-1 and MuRF1, as well as genes involved in apoptosis and autophagy were upregulated in muscles in CHF rats. Exercise inhibited muscle atrophy and skeletal muscle apoptosis.VEGFB and its receptor NRPI decreased significantly in CHF muscle. Exercise promoted VEGFB and NRP1 expression in cardiac tissue, gastrocnemius. Conclusions Exercise ameliorates CHF rat cardiac function. VEGFB inhibits cardiac muscle and gastrocnemius apoptosis in CHF rats

PO-183 Effect of Treadmill Running on Brown Adipose Tissue of Heart Failure Rats Induced by Abdominal Aortic Constriction

Objective To observe the effect of treadmill exercise on brown adipose tissue of heart failure rats induced by abdominal aortic constriction(AAC).  Methods 210g healthy male SD rats were randomly divided into control group and AAC group，after 4 weeks abdominal aortic constriction rats were selected and randomly divided into AAC group and treadmill running group. The exercise rats underwent treadmill running at 12m/s (40 min each, for 4 weeks). Real-time PCR and immunohistochemistry were used to detect the mRNA content and protein expression of cardiac ANP, BNP, and pgc1-a, ucp-1, leptin and adiponectin of the brown adipose tissue respectively. Results The rats with abdominal aortic constriction developed significant heart failure with preserved LV ejection fraction, increased LVAW d and LVID s. Compared with the control group, the myocardium levels of ANP and BNP in AAC group were significantly up-regulated. In the operation group, the function of brown adipose was enhanced. The volume of brown adipose cells decreased, the number of lipid droplet increased. The mRNA levels of UPC-1 and PGC1-a were significantly up-regulated, and the mRNA levels of leptin and adiponectin were down-regulated. In the exercise group, the browning of brown adipose was reduced, and the mRNA levels of UPC-1 and PGC1-a were decreased. Conclusions Exercise can affect the function of brown adipose tissue in heart failure rats induced by abdominal aortic constriction