39 papers
ID-LoRA is a novel framework that unifies audio and video personalization in a single generative pass by adapting the LTX-2 diffusion backbone with identity-driven In-Context LoRA and specialized techniques like negative temporal positions and identity guidance, achieving superior voice and visual likeness with minimal training data.
This paper introduces a few-shot image fusion framework that leverages a Granular Ball Pixel Computation algorithm to generate adaptive, confidence-aware incomplete priors, enabling a lightweight neural network to learn effective fusion rules from minimal data without requiring real fused images for supervision.
This paper introduces FontUse, a data-centric approach that leverages a large-scale, automatically annotated dataset of 70K images with style- and use-case-conditioned prompts to fine-tune text-to-image models, significantly improving their ability to generate typography that accurately reflects requested visual attributes without requiring architectural changes.
This paper presents Co-Layout, a novel framework that integrates large language models with grid-based integer programming and a coarse-to-fine optimization strategy to jointly optimize room layouts and furniture placement, significantly outperforming existing two-stage pipelines in both solution quality and computational efficiency.
PixARMesh is a novel autoregressive method that directly reconstructs complete, high-fidelity, and artist-ready 3D indoor scene meshes from a single RGB image by jointly predicting object layout and geometry within a unified model, eliminating the need for implicit fields or post-hoc optimization.
This paper introduces Icarus, a deep learning-based all-weather sky model that overcomes the limitations of existing methods in handling full dynamic range and class-imbalanced solar regions to generate photorealistic, user-controllable environment maps for accurate Image-Based Lighting.
This paper introduces Physical Simulator In-the-loop Video Generation (PSIVG), a framework that integrates a physical simulator and a test-time texture optimization technique into the video diffusion process to generate visually realistic videos that strictly adhere to real-world physical laws like gravity and collision.
This paper examines how the rapid advancement of AI, particularly with foundation models and unstructured data, introduces new challenges in latency, scalability, and interpretability for human-data interaction, arguing for a paradigm shift that redefines human-machine roles and integrates cognitive and perceptual principles to build more effective, human-centered analytical systems.
This paper introduces "Companion," an embodied AI system that integrates a drawing robot with Large Language Models to facilitate bidirectional, speech-and-sketch-based co-creation, transforming the robot from a passive tool into a playful artistic partner capable of generating professionally recognized visual stories.
The paper introduces altiro3D, a free library that synthesizes realistic 3D experiences and novel views from a single RGB image or video by combining monocular depth estimation, inpainting, and optimized projection algorithms to generate multi-viewpoint light fields for free-view displays.
This paper proposes a novel, resolution-independent, transformer-based inpainting module that utilizes spatio-temporal embeddings and adaptive patch selection to effectively complete missing textures in real-time 3D streaming from sparse multi-camera setups, achieving superior quality-speed trade-offs compared to existing methods.
This paper introduces a novel 5D spatio-directional neural encoding that generalizes hash-grid approaches to the directional domain using hierarchical geodesic grids, achieving state-of-the-art variance reduction in neural path guiding by effectively representing all-frequency signals without the distortions found in traditional methods.
This paper introduces a novel camera model that extends orthographic projection with a shrinkage parameter to effectively capture perspective distortion in close-up monocular 3D Morphable Model regression, enabling stable and accurate fitting for head-mounted camera footage.
GloSplat is a novel 3D reconstruction framework that achieves faster and more accurate results by performing joint pose-appearance optimization during 3D Gaussian Splatting training, uniquely preserving explicit SfM feature tracks as separate optimizable parameters to prevent pose drift and enable fine-grained refinement.
RealWonder is the first real-time system that generates action-conditioned videos from a single image by bridging 3D reconstruction, physics simulation, and a distilled video generator to simulate the physical consequences of forces, robotic manipulations, and camera controls on various materials.
This paper proposes SSR-GS, a novel framework that enhances glossy surface reconstruction in 3D Gaussian Splatting by modeling direct and indirect specular reflections through a prefiltered Mip-Cubemap and an IndiASG module, while employing Visual Geometry Priors to mitigate the impact of reflections on photometric loss.
The paper introduces "Gaussian Wardrobe," a novel framework that utilizes a compositional 3D Gaussian representation to disentangle shape-agnostic garment layers from human bodies, enabling high-fidelity photorealistic avatars and versatile virtual try-on applications with free-form clothing transfer.
NeuralRemaster introduces Phase-Preserving Diffusion (Ï-PD), a model-agnostic method that replaces standard Gaussian noise with phase-preserving, magnitude-randomized noise to enable structure-aligned image and video generation without architectural changes or inference-time costs.
This paper introduces HIT-Leiden, a novel algorithm that efficiently maintains high-quality Leiden communities in large dynamic graphs by bounding the scope of updates through hierarchical structures, achieving speedups of up to five orders of magnitude over existing methods while meeting stringent production latency requirements.