44 research outputs found
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
Despite recent research advancements in reconstructing clothed humans from a
single image, accurately restoring the "unseen regions" with high-level details
remains an unsolved challenge that lacks attention. Existing methods often
generate overly smooth back-side surfaces with a blurry texture. But how to
effectively capture all visual attributes of an individual from a single image,
which are sufficient to reconstruct unseen areas (e.g., the back view)?
Motivated by the power of foundation models, TeCH reconstructs the 3D human by
leveraging 1) descriptive text prompts (e.g., garments, colors, hairstyles)
which are automatically generated via a garment parsing model and Visual
Question Answering (VQA), 2) a personalized fine-tuned Text-to-Image diffusion
model (T2I) which learns the "indescribable" appearance. To represent
high-resolution 3D clothed humans at an affordable cost, we propose a hybrid 3D
representation based on DMTet, which consists of an explicit body shape grid
and an implicit distance field. Guided by the descriptive prompts +
personalized T2I diffusion model, the geometry and texture of the 3D humans are
optimized through multi-view Score Distillation Sampling (SDS) and
reconstruction losses based on the original observation. TeCH produces
high-fidelity 3D clothed humans with consistent & delicate texture, and
detailed full-body geometry. Quantitative and qualitative experiments
demonstrate that TeCH outperforms the state-of-the-art methods in terms of
reconstruction accuracy and rendering quality. The code will be publicly
available for research purposes at https://huangyangyi.github.io/TeCHComment: Project: https://huangyangyi.github.io/TeCH, Code:
https://github.com/huangyangyi/TeC
TADA! Text to Animatable Digital Avatars
We introduce TADA, a simple-yet-effective approach that takes textual
descriptions and produces expressive 3D avatars with high-quality geometry and
lifelike textures, that can be animated and rendered with traditional graphics
pipelines. Existing text-based character generation methods are limited in
terms of geometry and texture quality, and cannot be realistically animated due
to inconsistent alignment between the geometry and the texture, particularly in
the face region. To overcome these limitations, TADA leverages the synergy of a
2D diffusion model and an animatable parametric body model. Specifically, we
derive an optimizable high-resolution body model from SMPL-X with 3D
displacements and a texture map, and use hierarchical rendering with score
distillation sampling (SDS) to create high-quality, detailed, holistic 3D
avatars from text. To ensure alignment between the geometry and texture, we
render normals and RGB images of the generated character and exploit their
latent embeddings in the SDS training process. We further introduce various
expression parameters to deform the generated character during training,
ensuring that the semantics of our generated character remain consistent with
the original SMPL-X model, resulting in an animatable character. Comprehensive
evaluations demonstrate that TADA significantly surpasses existing approaches
on both qualitative and quantitative measures. TADA enables creation of
large-scale digital character assets that are ready for animation and
rendering, while also being easily editable through natural language. The code
will be public for research purposes
One-shot Implicit Animatable Avatars with Model-based Priors
Existing neural rendering methods for creating human avatars typically either
require dense input signals such as video or multi-view images, or leverage a
learned prior from large-scale specific 3D human datasets such that
reconstruction can be performed with sparse-view inputs. Most of these methods
fail to achieve realistic reconstruction when only a single image is available.
To enable the data-efficient creation of realistic animatable 3D humans, we
propose ELICIT, a novel method for learning human-specific neural radiance
fields from a single image. Inspired by the fact that humans can effortlessly
estimate the body geometry and imagine full-body clothing from a single image,
we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior.
Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned
vertex-based template model (i.e., SMPL) and implements the visual clothing
semantic prior with the CLIP-based pretrained models. Both priors are used to
jointly guide the optimization for creating plausible content in the invisible
areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to
generate text-conditioned unseen regions. In order to further improve visual
details, we propose a segmentation-based sampling strategy that locally refines
different parts of the avatar. Comprehensive evaluations on multiple popular
benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT
has outperformed strong baseline methods of avatar creation when only a single
image is available. The code is public for research purposes at
https://huangyangyi.github.io/ELICIT/.Comment: To appear at ICCV 2023. Project website:
https://huangyangyi.github.io/ELICIT
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
Textual adversarial attacks can discover models' weaknesses by adding
semantic-preserved but misleading perturbations to the inputs. The long-lasting
adversarial attack-and-defense arms race in Natural Language Processing (NLP)
is algorithm-centric, providing valuable techniques for automatic robustness
evaluation. However, the existing practice of robustness evaluation may exhibit
issues of incomprehensive evaluation, impractical evaluation protocol, and
invalid adversarial samples. In this paper, we aim to set up a unified
automatic robustness evaluation framework, shifting towards model-centric
evaluation to further exploit the advantages of adversarial attacks. To address
the above challenges, we first determine robustness evaluation dimensions based
on model capabilities and specify the reasonable algorithm to generate
adversarial samples for each dimension. Then we establish the evaluation
protocol, including evaluation settings and metrics, under realistic demands.
Finally, we use the perturbation degree of adversarial samples to control the
sample validity. We implement a toolkit RobTest that realizes our automatic
robustness evaluation framework. In our experiments, we conduct a robustness
evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation
framework, and further show the rationality of each component in the framework.
The code will be made public at \url{https://github.com/thunlp/RobTest}.Comment: Accepted to Findings of ACL 202
Activation of Interleukin-1β Release by the Classical Swine Fever Virus Is Dependent on the NLRP3 Inflammasome, Which Affects Virus Growth in Monocytes
Classical swine fever virus (CSFV) is a classic Flavivirus that causes the acute, febrile, and highly contagious disease known as classical swine fever (CSF). Inflammasomes are molecular platforms that trigger the maturation of proinflammatory cytokines to engage innate immune defenses that are induced upon cellular infection or stress. However, the relationship between the inflammasome and CSFV infection has not been thoroughly characterized. To understand the function of the inflammasome response to CSFV infection, we infected porcine peripheral blood monocytes (PBMCs) with CSFV. Our results indicated that CSFV infection induced both the generation of pro-interleukin-1β (pro-IL-1β) and its processing in monocytes, leading to the maturation and secretion of IL-1β through the activation of caspase 1. Moreover, CSFV infection in PBMCs induced the production and cleavage of gasdermin D (GSDMD), which is an inducer of pyroptosis. Additional studies showed that CSFV-induced IL-1β secretion was mediated by NLRP3 and that CSFV infection could sufficiently activate the assembly of the NLRP3 inflammasome in monocytes. These results revealed that CSFV infection inhibited the expression of NLRP3, and knockdown of NLRP3 enhanced the replication of CSFV. In conclusion, these findings demonstrate that the NLRP3 inflammasome plays an important role in the innate immune response to CSFV infection
31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two
Background
The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd.
Methods
We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background.
Results
First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001).
Conclusions
In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival
A Neglected Issue: Stationary Phase Retention Determination of Classic High-Speed Counter-Current Chromatography Solvent Systems
Obtaining an ideal solvent system for target compounds is still an obstacle to the wide application of high-speed counter-current chromatography (HSCCC). The partition coefficient and retention of the stationary phase are two key parameters for solvent system selection. The retention of the stationary phase of the solvent system is roughly judged by settling time using a test tube, which is subjective and inaccurate. In this study, we demonstrated that high-resolution separation of HSCCC is tightly connected with the retention of the stationary phase. Notably, unlike the in vitro test of settling time, we investigated the retention of the stationary phase of classical biphasic solvent systems by a TBE300C HSCCC apparatus. Our results revealed that settling time is not always inversely proportional to the retention of the stationary phase. The n-hexane–ethylacetate–methanol–water solvent systems showed the highest correlation coefficient of settling time and retention of the stationary phase (r = −0.91, n = 16). N-heptane–n-butanol–acetonitrile–water solvent system showed the lowest correlation coefficient (r = −0.26, n = 7). These results may be helpful for HSCCC solvent system selection and accelerate the application of this technique