DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling

Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors. Despite significant progress, existing methods struggle with maintaining high quality, consistency, and editing flexibility. In this paper, we propose DreamScene, a 3D Gaussian-based novel text-to-3D scene generation framework, to tackle the aforementioned three challenges mainly via two strategies. First, DreamScene employs Formation Pattern Sampling (FPS), a multi-timestep sampling strategy guided by the formation patterns of 3D objects, to form fast, semantically rich, and high-quality representations. FPS uses 3D Gaussian filtering for optimization stability, and leverages reconstruction techniques to generate plausible textures. Second, DreamScene employs a progressive three-stage camera sampling strategy, specifically designed for both indoor and outdoor settings, to effectively ensure object-environment integration and scene-wide 3D consistency. Last, DreamScene enhances scene editing flexibility by integrating objects and environments, enabling targeted adjustments. Extensive experiments validate DreamScene's superiority over current state-of-the-art techniques, heralding its wide-ranging potential for diverse applications. Code and demos will be released at https://dreamscene-project.github.io .

DeCoF: Generated Video Detection via Frame Consistency

The escalating quality of video generated by advanced video generation methods leads to new security challenges in society, which makes generated video detection an urgent research priority. To foster collaborative research in this area, we construct the first open-source dataset explicitly for generated video detection, providing a valuable resource for the community to benchmark and improve detection methodologies. Through a series of carefully designed probe experiments, our study explores the significance of temporal and spatial artifacts in developing general and robust detectors for generated video. Based on the principle of video frame consistency, we introduce a simple yet effective detection model (DeCoF) that eliminates the impact of spatial artifacts during generalizing feature learning. Our extensive experiments demonstrate the efficacy of DeCoF in detecting videos produced by unseen video generation models and confirm its powerful generalization capabilities across several commercial proprietary models.

Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise

Neural radiance fields (NeRF) have been proposed as an innovative 3D representation method. While attracting lots of attention, NeRF faces critical issues such as information confidentiality and security. Steganography is a technique used to embed information in another object as a means of protecting information security. Currently, there are few related studies on NeRF steganography, facing challenges in low steganography quality, model weight damage, and a limited amount of steganographic information. This paper proposes a novel NeRF steganography method based on trainable noise: Noise-NeRF. Furthermore, we propose the Adaptive Pixel Selection strategy and Pixel Perturbation strategy to improve the steganography quality and efficiency. The extensive experiments on open-source datasets show that Noise-NeRF provides state-of-the-art performances in both steganography quality and rendering quality, as well as effectiveness in super-resolution image steganography.

2D-Guided 3D Gaussian Segmentation

Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration. These advantages signal a wide range of applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the segmentation of 3D Gaussians is still in its infancy. The existing segmentation methods are not only cumbersome but also incapable of segmenting multiple objects simultaneously in a short amount of time. In response, this paper introduces a 3D Gaussian segmentation method implemented with 2D segmentation as supervision. This approach uses input 2D segmentation maps to guide the learning of the added 3D Gaussian semantic information, while nearest neighbor clustering and statistical filtering refine the segmentation results. Experiments show that our concise method can achieve comparable performances on mIOU and mAcc for multi-object segmentation as previous single-object segmentation methods.

FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion

Knowledge graph completion (KGC) aims to predict missing facts in knowledge graphs (KGs), which is crucial as modern KGs remain largely incomplete. While training KGC models on multiple aligned KGs can improve performance, previous methods that rely on transferring raw data among KGs raise privacy concerns. To address this challenge, we propose a new federated learning framework that implicitly aggregates knowledge from multiple KGs without demanding raw data exchange and entity alignment. We treat each KG as a client that trains a local language model through textbased knowledge representation learning. A central server then aggregates the model weights from clients. As natural language provides a universal representation, the same knowledge thus has similar semantic representations across KGs. As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing. Extensive experiments on a benchmark dataset demonstrate that our method substantially improves KGC on multilingual KGs, achieving comparable performance to state-of-the-art alignment-based models without requiring any labeled alignments or raw user data sharing. Our codes will be publicly available.

Take History as a Mirror in Heterogeneous Federated Learning

Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practice, due to the system and statistical heterogeneity among devices, synchronous FL often encounters the straggler effect. In contrast, asynchronous FL can mitigate this problem, making it suitable for scenarios involving numerous participants. However, Non-IID data and stale models present significant challenges to asynchronous FL, as they would diminish the practicality of the global model and even lead to training failures. In this work, we propose a novel asynchronous FL framework called Federated Historical Learning (FedHist), which effectively addresses the challenges posed by both Non-IID data and gradient staleness. FedHist enhances the stability of local gradients by performing weighted fusion with historical global gradients cached on the server. Relying on hindsight, it assigns aggregation weights to each participant in a multi-dimensional manner during each communication round. To further enhance the efficiency and stability of the training process, we introduce an intelligent $\ell_2$-norm amplification scheme, which dynamically regulates the learning progress based on the $\ell_2$-norms of the submitted gradients. Extensive experiments demonstrate that FedHist outperforms state-of-the-art methods in terms of convergence performance and test accuracy.

3D-GOI: 3D GAN Omni-Inversion for Multifaceted and Multi-object Editing

The current GAN inversion methods typically can only edit the appearance and shape of a single object and background while overlooking spatial information. In this work, we propose a 3D editing framework, 3D-GOI, to enable multifaceted editing of affine information (scale, translation, and rotation) on multiple objects. 3D-GOI realizes the complex editing function by inverting the abundance of attribute codes (object shape/appearance/scale/rotation/translation, background shape/appearance, and camera pose) controlled by GIRAFFE, a renowned 3D GAN. Accurately inverting all the codes is challenging, 3D-GOI solves this challenge following three main steps. First, we segment the objects and the background in a multi-object image. Second, we use a custom Neural Inversion Encoder to obtain coarse codes of each object. Finally, we use a round-robin optimization algorithm to get precise codes to reconstruct the image. To the best of our knowledge, 3D-GOI is the first framework to enable multifaceted editing on multiple objects. Both qualitative and quantitative experiments demonstrate that 3D-GOI holds immense potential for flexible, multifaceted editing in complex multi-object scenes.

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.