Flex3D: Feed-Forward 3D Generation With Flexible Reconstruction Model
  And Input View Curation
Generating high-quality 3D content from text, single images, or sparse view
images remains a challenging task with broad applications.Existing methods
typically employ multi-view diffusion models to synthesize multi-view images,
followed by a feed-forward process for 3D reconstruction. However, these
approaches are often constrained by a small and fixed number of input views,
limiting their ability to capture diverse viewpoints and, even worse, leading
to suboptimal generation results if the synthesized views are of poor quality.
To address these limitations, we propose Flex3D, a novel two-stage framework
capable of leveraging an arbitrary number of high-quality input views. The
first stage consists of a candidate view generation and curation pipeline. We
employ a fine-tuned multi-view image diffusion model and a video diffusion
model to generate a pool of candidate views, enabling a rich representation of
the target 3D object. Subsequently, a view selection pipeline filters these
views based on quality and consistency, ensuring that only the high-quality and
reliable views are used for reconstruction. In the second stage, the curated
views are fed into a Flexible Reconstruction Model (FlexRM), built upon a
transformer architecture that can effectively process an arbitrary number of
inputs. FlemRM directly outputs 3D Gaussian points leveraging a tri-plane
representation, enabling efficient and detailed 3D generation. Through
extensive exploration of design and training strategies, we optimize FlexRM to
achieve superior performance in both reconstruction and generation tasks. Our
results demonstrate that Flex3D achieves state-of-the-art performance, with a
user study winning rate of over 92% in 3D generation tasks when compared to
several of the latest feed-forward 3D generative models.
66fcb7a57d19fc1946b9029f
Junlin Han
admin_assigned
2024-10-02T09:01:05.751Z
636e6ee287545ca5a136b4c3
/avatars/208d32b1202e2da210146027212dbdd3.svg
Junlin Han
user
Junlinh
66fcb7a57d19fc1946b902a0
Jianyuan Wang
admin_assigned
2024-10-02T09:00:38.894Z
649bf403fd9cea8366d669ad
/avatars/27bd8ca9a948ec38fee950b64f669ce3.svg
Jianyuan Wang
user
JianyuanWang
66fcb7a57d19fc1946b902a1
Andrea Vedaldi
66fcb7a57d19fc1946b902a2
Philip Torr
admin_assigned
2024-10-02T09:00:23.357Z
6565ed28a5ec0231cb07225f
/avatars/7f95bba9aa7811d56eecb380827abfac.svg
prof philip torr
user
philiptorr
66fcb7a57d19fc1946b902a3
Filippos Kokkinos
Flex3D: Feed-Forward 3D Generation With Flexible Reconstruction Model
  And Input View Curation
Generating high-quality 3D content from text, single images, or sparse view
images remains a challenging task with broad applications.Existing methods
typically employ multi-view diffusion models to synthesize multi-view images,
followed by a feed-forward process for 3D reconstruction. However, these
approaches are often constrained by a small and fixed number of input views,
limiting their ability to capture diverse viewpoints and, even worse, leading
to suboptimal generation results if the synthesized views are of poor quality.
To address these limitations, we propose Flex3D, a novel two-stage framework
capable of leveraging an arbitrary number of high-quality input views. The
first stage consists of a candidate view generation and curation pipeline. We
employ a fine-tuned multi-view image diffusion model and a video diffusion
model to generate a pool of candidate views, enabling a rich representation of
the target 3D object. Subsequently, a view selection pipeline filters these
views based on quality and consistency, ensuring that only the high-quality and
reliable views are used for reconstruction. In the second stage, the curated
views are fed into a Flexible Reconstruction Model (FlexRM), built upon a
transformer architecture that can effectively process an arbitrary number of
inputs. FlemRM directly outputs 3D Gaussian points leveraging a tri-plane
representation, enabling efficient and detailed 3D generation. Through
extensive exploration of design and training strategies, we optimize FlexRM to
achieve superior performance in both reconstruction and generation tasks. Our
results demonstrate that Flex3D achieves state-of-the-art performance, with a
user study winning rate of over 92% in 3D generation tasks when compared to
several of the latest feed-forward 3D generative models.
Flex3D generates high-quality 3D content from diverse input views using a two-stage framework with image and video diffusion models, followed by a transformer-based flexible reconstruction model.
multi-view diffusion models video diffusion model candidate view generation view selection pipeline Flexible Reconstruction Model FlexRM transformer architecture 3D Gaussian points tri-plane representation