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alex yu s website alex yu personal website about google scholar sxyu hey my name is alex and i am an ai researcher and founder based in san francisco currently working on image generation previously i was the co founder and cto at luma ai where i worked on generative models in particular ray aka dream machine a frontier video generative model and also genie a text to 3d model before that i graduated from uc berkeley where i worked on research with prof angjoo kanazawa foundation models and research works dream machine june 2024 website a generative video model that generates 120 frames of high fidelity video from a image or text prompt in under 120 seconds built on a transformer architecture and trained directly on videos dream machine shows a promising level of 3d and physical consistency we followed this with advanced control capabilities such as keyframe control and looping genie text to 3d foundation model november 2023 website a foundation model for generating 3d assets from a text prompt within 10 seconds along with a refine process to achieve higher quality models plenoxels radiance fields without neural networks alex yu sara fridovich keil matt tancik qinhong chen benjamin recht angjoo kanazawa cvpr 2022 oral arxiv website video two minute papers we introduce plenoxels plenoptic voxels a system for photorealistic view synthesis plenoxels represent a scene as a sparse 3d grid with spherical harmonics this representation can be optimized from calibrated images via gradient methods and regularization without any neural components on standard benchmark tasks plenoxels are optimized two orders of magnitude faster than neural radiance fields with no loss in visual quality plenoctrees for real time rendering of neural radiance fields alex yu ruilong li matt tancik hao li ren ng angjoo kanazawa iccv 2021 oral arxiv website video we introduce a method to render neural radiance fields nerfs in real time using plenoctrees an octree based 3d representation which supports view dependent effects our method can render 800x800 images at more than 150 fps which is over 3000 times faster than conventional nerfs we do so without sacrificing quality while preserving the ability of nerfs to perform free viewpoint rendering of scenes with arbitrary geometry and view dependent effects real time performance is achieved by pre tabulating the nerf into a plenoctree in order to preserve view dependent effects such as specularities we factorize the appearance via closed form spherical basis functions specifically we show that it is possible to train nerfs to predict a spherical harmonic representation of radiance removing the viewing direction as an input to the neural network furthermore we show that plenoctrees can be directly optimized to further minimize the reconstruction loss which leads to equal or better quality compared to competing methods moreover this octree optimization step can be used to reduce the training time as we no longer need to wait for the nerf training to converge fully our real time neural rendering approach may potentially enable new applications such as 6 dof industrial and product visualizations as well as next generation ar vr systems plenoctrees are amenable to in browser rendering as well please visit the project page for the interactive online demo pixelnerf neural radiance fields from one or few images alex yu vickie ye matt tancik angjoo kanazawa cvpr 2021 arxiv website video we propose pixelnerf a learning framework that predicts a continuous neural scene representation conditioned on one or few input images the existing approach for constructing neural radiance fields involves optimizing the representation to every scene independently requiring many calibrated views and significant compute time we take a step towards resolving these shortcomings by introducing an architecture that conditions a nerf on image inputs in a fully convolutional manner this allows the network to be trained across multiple scenes to learn a scene prior enabling it to perform novel view synthesis in a feed forward manner from a sparse set of views as few as one the breakthrough listen search for intelligent life data formats reduction and archiving matthew lebofsky et al publications of the astronomical society of the pacific pasp arxiv breakthrough listen is the most comprehensive and sensitive search for extraterrestrial intelligence seti to date employing a collection of international observational facilities including both radio and optical telescopes during the first three years of the listen program thousands of targets have been observed with the green bank telescope gbt parkes telescope and automated planet finder at gbt and parkes observations have been performed ranging from 700 mhz to 26 ghz with raw data volumes averaging over 1pb day in this paper we describe the hardware and software pipeline used for collection reduction archival and public dissemination of listen data equal contribution 2022 2024 luma ai co founder series b ai start up based in palo alto summer 2020 fall 2021 berkeley ai research research assistant advisor angjoo kanazawa worked on research related to nerfs see below summer 2021 adobe research research intern host oliver wang worked on nerf without colmap fall 2019 cs 61a uc berkeley teaching assistant responsible for the traditional hog contest for several semesters before summer 2019 google swe intern built banking and trading features for google assistant spring 2019 seti breakthrough listen urap apprentice looking for alien technosignatures at seti fall 2017 fall 2019 fhl vive center research assistant worked on human and hand projects in openark with dr allen yang source code for this website