"HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion"... Constructs temporal NeRF of humans from multi-view video Impressive quality. Easy to see applications in gaming - better avatar creation etc.show more

📢DNF: Generating 4D animations with dictionary-based neural fields! Xinyi... Zhang presents a new dictionary-based neural field for unconditional 4D generation of deforming shapes -- generating motions with high-quality shape and temporal consistency.show more

This is preserved anatomy with Ray3. From faces and... hands to bodies in motion, every form stays true. Humans and creatures remain clear and coherent in every action. Retain character structure with high fidelity, holding details together even through complex cinematic scenes.show more

DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from... In-the-Wild Drone Imagery Abstract: Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.show more

Can’t wait for humans to become multi planetary

Are you at #SIGGRAPH2024 and want to learn how... to reconstruct meshes from multi-view images that contain details like individual strands of grass? Then come today to the "Radiance Field Processing" session at 2 p.m. in Mile High 1.show more

Moravec's Paradox is real. What's hard for humans is... easy for robots. What's easy for humans is hard for robots. Crazy flip kick but still no humanoids doing useful work in production.show more

Cinematic Motion Prompts for Fashion The following prompt examples... leverage the composition of our starting image to create high-fidelity video motion. Let's go 👇show more

Humans drink milk to see better at night 🐾

Vidu Q3 is now available on Akool Experience next-level... AI video creation with Vidu Q3 — a powerful multi-modal model that seamlessly blends high-fidelity visuals with perfectly synchronized audio. Powered by Vidu AI, Q3 shines in: Deep narrative understanding Mastery of complex cinematic language Lifelike, human-feeling motion and emotion If storytelling and cinematic quality matter to you, Vidu Q3 is built for it. Try it now on Akool.show more

This is a NeRF (Neural Radiance Field) and it's... one of the coolest pieces of AI tech out there. To generate this scene, I shot a video on my iPhone, then uploaded to Luma Luma turned it into a NeRF, and then I directed the camera in their editor to create this shot. Don't sleep on NeRFs.show more

Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic... Gaussians paper page: Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups. In this paper, we propose Gaussian Head Avatar represented by controllable 3D Gaussians for high-fidelity head avatar modeling. We optimize the neutral 3D Gaussians and a fully learned MLP-based deformation field to capture complex expressions. The two parts benefit each other, thereby our method can model fine-grained dynamic details while ensuring expression accuracy. Furthermore, we devise a well-designed geometry-guided initialization strategy based on implicit SDF and Deep Marching Tetrahedra for the stability and convergence of the training procedure. Experiments show our approach outperforms other state-of-the-art sparse-view methods, achieving ultra high-fidelity rendering quality at 2K resolution even under exaggerated expressions.show more

LongCat-Video-Avatar 1.5 is a major upgrade for AI avatar... generation. > Better lip-sync + mouth-shape accuracy > Stable identities across long videos & multi-person scenes > Smooth full-body motion, singing, animation, & hand interactions > Outperforms HeyGen, Kling Avatar 2.0, & OmniHuman-1.5 in consistency tests Free & open sourceshow more

We will be in ACM SIGGRAPH 2023 with "3D... Gaussian Splatting for Real-Time Radiance Field Rendering", have you ever seen radiance fields with 100+ FPS and MipNeRF360 quality? Check out our website here:show more

Rare high view of the building left over from... a previous simulation that humans here did not build and has false documentation of its "632 year" construction to cover its tracks so no one realizes it. In my humble opinion of course.show more

One of the most frightening videos i came across... The complete oppression of beings that never should be in the hands or in hideous constructs of humans #EndAnimalExploitation #LiveVegan find your empathy and revive it ✌️show more

We just streamed the first ultra-HD video brought to... you via laser from deep space. And it’s a video of Taters, a tabby cat. This test will pave the way for high-data-rate communications in support of the next giant leap: sending humans to Mars.show more

Quality of life improves when you listen to birds... more than you do humansshow more

This is RICULOUSLY good, TRELLIS 3D Generation model by... Microsoft! 🔥 Generate high-quality 3D assets from text or image prompts. Supports various formats like Radiance Fields, 3D Gaussians, and meshes Available for FREE on Hugging Face!show more

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper... page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.show more