基于物理條件約束的可信視覺生成大模型-培訓(xùn)課件_第1頁
基于物理條件約束的可信視覺生成大模型-培訓(xùn)課件_第2頁
基于物理條件約束的可信視覺生成大模型-培訓(xùn)課件_第3頁
基于物理條件約束的可信視覺生成大模型-培訓(xùn)課件_第4頁
基于物理條件約束的可信視覺生成大模型-培訓(xùn)課件_第5頁
已閱讀5頁,還剩28頁未讀 繼續(xù)免費閱讀

下載本文檔

版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請進行舉報或認領(lǐng)

文檔簡介

基于物理條件約束的可信視覺生成大模型Visual

generative

modelInputOutputVAE:

maximize

variationallowerboundVideo

generative

methods?

Thefieldofvideo

generationhasseenrapiddevelopment,

reachingseveralmilestones...VAE:

maximize

variationallowerboundGAN:

AdversarialtrainingFlow-based

models:

Invertible

transform

ofDiffusionmodels:

GraduallyaddGaussian

noisedistributionsandthenreverseDiffusion

for

visual

generation

(1)?

DenoisingDiffusion

Probabilistic

Models

(DDPMs)Diffusion

for

visual

generation

(2)?

Stochastic

Differential

Equations

(Score

SDEs)Key

Elements

of

visual

Diffusion

Models?

Pixel

diffusion

(originalinput)?

Latent

spacediffusion?

Unet?

TransformerSora,

breakthrough?

Consistency:consistencyin3Drendering,long-rangecoherence,

andobjectpermanence.?

Highfidelity.?

Surprisinglength:extended

videolength

capability(Sora:

1

minutevs.previous

systems:

seconds).?

Flexible

resolution:generation

ofvideosacross

various

durations,aspectratios,

andresolutions.Sora,

key

technologies?

TheDiTframework

by

Meta

(2022.12)is

designedfor

videoprocessing.?

Google's

MAGViT

(2022.12)focuses

onVideoTokenization.?

GoogleDeepMindintroduced

NaViT(2023.07)to

supportvariousresolutions

andaspectratios.?

OpenAI's

DALL-E

3

(2023.09)enhancesVideoCaptiongeneration

forimproved

conditioned

videocreation.Modeling

the

physical

world?

We

knowthat

itis

verycomplicated

real

physical

model.probabilistic?

bayesian

inference;?

probabilisticgraphical

models.deterministic?

mathematicalequations;?

physics

basedsimulation;?

control

theory.Modeling

the

physical

world?

We

knowthatitisverycomplicatedrealphysicalmodel.probabilistic?

bayesian

inference;?

probabilisticgraphical

models.deterministic?

mathematicalequations;?

physics

basedsimulation;?

control

theory.Key

elements

of

a

physical

world?

GivenaSora

demo(thewalkingwomanintheTokyo

street),thekey

elementsofaphysicalworld,inthegraphicalway...?

Appearance?

Geometry?

Lighting?

Motion&Animation?

AudioModeling

the

physical

world?

[CVPR]Gaussian-Flow:4DReconstructionwithDynamic3DGaussianParticleEspressoChick-ChickenSplit-CookieFlame-SteakModeling

the

physical

world?

[CVPR]Gaussian-Flow:4DReconstructionwithDynamic3DGaussianParticleIt

is

hard

to

model

the

physical

world?

In

fact,

theworld

ishard

to

modelina

probablistic

way.?

Sora

resource

consumption...–

1billionsofimages;–

1millionsofhoursofvideo

data;–

10trillionstokens

aftertokenizingimagesandvideos–

Training

with~5,000A100sinparallel.It

is

hard

to

model

the

physical

world?

Sora

failure

casein

geometryandappearance.It

is

hard

to

model

the

physical

world?

Sora

failure

case

inlighting.It

is

hard

to

model

the

physical

world?

Sora

failure

case

inmotionandanimation.It

is

hard

to

model

the

physical

world?

VideoMV:ConsistentMulti-ViewGenerationBasedonLarge

VideoGenerativeModel?

Geometricenhancementisstillneededfor

multi-viewimages.It

is

hard

to

model

the

physical

world?

VideoMV:ConsistentMulti-ViewGenerationBasedonLarge

VideoGenerativeModel?

Fromastatic

aspects,SVDisabletomodelmulti-viewimages.It

is

hard

to

model

the

physical

world?

Stag4D:Spatial-Temporal

AnchoredGenerative4DGaussians?

From

atemporalaspects...It

is

hard

to

model

the

physical

world?

STAG4D:

Spatial-Temporal

AnchoredGenerative4DGaussians?

Fromatemporal

aspects...It

is

hard

to

model

the

physical

world?

Ilya

Sutskever:

compression

is

generalization.?

Thebest

losslesscompression

for

adataset

is

thebestgeneralization

for

data

outsidethedataset.Apply

the

deterministic

conditions?

Different

representationsof

deterministicconditionsinthephysicalworld.?

Muchlessdata

andparameters!GeometryLightingMotion&AnimationApply

the

deterministic

conditions?

Thereare

two

ways

to

injectdeterministicinformation.deterministic#1deterministic#2Image

Human

Animation?

Champ:

Controllable

andConsistent

HumanImage

Animation

with

3D

Parametric

GuidanceImage

Human

Animation?

Champ:

Controllable

andConsistent

HumanImage

Animation

with

3D

Parametric

GuidanceImage

Human

Animation?

Champ:

Controllable

andConsistent

HumanImage

Animation

with

3D

Parametric

GuidanceImage

Por

trait

Animation?

Hallo:

Hierarchical

Audio-Driven

VisualSynthesisfor

Portrait

Image

AnimationImage

Por

trait

Animation?

Hallo:

Hierarchical

Audio-Driven

VisualSynthesisfor

Portrait

Image

AnimationImage

Por

trait

Animation?

Hallo:

Hierarchical

Audio-Driven

VisualSynthesisfor

Portrait

Image

AnimationDynamic

Protein

Structure

Prediction?

4D

Diffusion

for

DynamicProtein

Structure

Prediction

with

Reference

GuidedTemporal

AlignmentDynamic

Protei

溫馨提示

  • 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
  • 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
  • 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
  • 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
  • 5. 人人文庫網(wǎng)僅提供信息存儲空間,僅對用戶上傳內(nèi)容的表現(xiàn)方式做保護處理,對用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對任何下載內(nèi)容負責。
  • 6. 下載文件中如有侵權(quán)或不適當內(nèi)容,請與我們聯(lián)系,我們立即糾正。
  • 7. 本站不保證下載資源的準確性、安全性和完整性, 同時也不承擔用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。

最新文檔

評論

0/150

提交評論