Many XR applications require the delivery of volumetric video to users with
six degrees of freedom (6-DoF) movements. Point Cloud has become a popular
volumetric video format. A dense point cloud consumes much higher bandwidth
than a 2D/360 degree video frame. User Field of View (FoV) is more dynamic with
6-DoF movement than 3-DoF movement. To save bandwidth, FoV-adaptive streaming
predicts a user’s FoV and only downloads point cloud data falling in the
predicted FoV. However, it is vulnerable to FoV prediction errors, which can be
significant when a long buffer is utilized for smoothed streaming. In this
work, we propose a multi-round progressive refinement framework for point cloud
video streaming. Instead of sequentially downloading point cloud frames, our
solution simultaneously downloads/patches multiple frames falling into a
sliding time-window, leveraging the inherent scalability of octree-based
point-cloud coding. The optimal rate allocation among all tiles of active
frames are solved analytically using the heterogeneous tile rate-quality
functions calibrated by the predicted user FoV. Multi-frame
downloading/patching simultaneously takes advantage of the streaming smoothness
resulting from long buffer and the FoV prediction accuracy at short buffer
length. We evaluate our streaming solution using simulations driven by real
point cloud videos, real bandwidth traces, and 6-DoF FoV traces of real users.
Our solution is robust against the bandwidth/FoV prediction errors, and can
deliver high and smooth view quality in the face of bandwidth variations and
dynamic user and point cloud movements.
Expert Commentary: The Multi-Disciplinary Nature of Point Cloud Video Streaming
Point cloud video streaming is an important aspect of multimedia information systems, as it enables the delivery of volumetric video with six degrees of freedom (6-DoF) movements to users. This technology is a multi-disciplinary field that combines concepts from animations, artificial reality, augmented reality, and virtual realities.
The article discusses the challenges of delivering point cloud videos, which consume higher bandwidth compared to traditional 2D or 360-degree videos. Additionally, the user’s field of view (FoV) is more dynamic with 6-DoF movement, making it necessary to optimize the streaming process to save bandwidth and provide a high-quality viewing experience.
To address these challenges, the proposed multi-round progressive refinement framework for point cloud video streaming is introduced. This framework simultaneously downloads and patches multiple frames falling into a sliding time-window, leveraging the scalability of octree-based point-cloud coding. By allocating the optimal rate among all tiles of active frames, the solution ensures high-quality viewability based on predicted user FoV.
The multi-disciplinary nature of this framework becomes evident when considering its various components. The use of point cloud videos brings in concepts from animations and 3D modeling, as it requires the representation of objects as a collection of points in 3D space. The integration of artificial reality, augmented reality, and virtual realities is crucial in understanding the user’s dynamic field of view and predicting their FoV accurately for optimized streaming.
From a multimedia information systems perspective, this framework addresses the challenge of delivering volumetric video effectively. Bandwidth efficiency is essential in multimedia systems, especially when dealing with resource-intensive formats like point clouds. By optimizing the rate allocation and leveraging the scalability of octree-based coding, the proposed solution tackles the bandwidth consumption issue and ensures a high-quality viewing experience.
The evaluation of the streaming solution using simulations driven by real point cloud videos, bandwidth traces, and 6-DoF FoV traces of real users demonstrates its robustness against bandwidth and FoV prediction errors. This is significant in the context of multimedia information systems, as it validates the effectiveness of the framework in delivering high and smooth view quality despite variations in bandwidth and dynamic user and point cloud movements.
In conclusion, point cloud video streaming is an area that intersects various disciplines within the field of multimedia information systems. The proposed multi-round progressive refinement framework addresses the challenges of delivering volumetric video with 6-DoF movements by optimizing rate allocation and leveraging octree-based coding. This solution demonstrates the multi-disciplinary nature of point cloud video streaming and its relevance to animations, artificial reality, augmented reality, and virtual realities.
Recently, text-guided scalable vector graphics (SVGs) synthesis has shown
promise in domains such as iconography and sketch. However, existing
text-to-SVG generation methods lack editability and struggle with visual
quality and result diversity. To address these limitations, we propose a novel
text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer
incorporates a semantic-driven image vectorization (SIVE) process that enables
the decomposition of synthesis into foreground objects and background, thereby
enhancing editability. Specifically, the SIVE process introduce attention-based
primitive control and an attention-mask loss function for effective control and
manipulation of individual elements. Additionally, we propose a Vectorized
Particle-based Score Distillation (VPSD) approach to tackle the challenges of
color over-saturation, vector primitives over-smoothing, and limited result
diversity in existing text-to-SVG generation methods. Furthermore, on the basis
of VPSD, we introduce Reward Feedback Learning (ReFL) to accelerate VPSD
convergence and improve aesthetic appeal. Extensive experiments have been
conducted to validate the effectiveness of SVGDreamer, demonstrating its
superiority over baseline methods in terms of editability, visual quality, and
diversity.
Analysis: The Multi-disciplinary Nature of SVGDreamer
SVGDreamer is a novel text-guided vector graphics synthesis method that addresses the limitations of existing text-to-SVG generation methods. This research introduces several innovative techniques that enhance the editability, visual quality, and result diversity of synthesized vector graphics. By incorporating a semantic-driven image vectorization (SIVE) process, SVGDreamer enables the decomposition of synthesis into foreground objects and background, thereby enhancing editability.
One notable aspect of SVGDreamer is its multi-disciplinary nature. It combines concepts from computer vision and natural language processing to achieve its objectives. The attention-based primitive control introduced in the SIVE process leverages computer vision techniques to effectively control and manipulate individual elements of the vector graphics. By incorporating an attention-mask loss function, SVGDreamer further enhances the control and ensures accurate synthesis.
Another significant contribution of SVGDreamer is the Vectorized Particle-based Score Distillation (VPSD) approach. This approach tackles several challenges commonly observed in existing text-to-SVG generation methods, such as over-saturation of colors, over-smoothing of vector primitives, and limited result diversity. By leveraging particle-based score distillation, SVGDreamer improves the visual quality of synthesized vector graphics, making them more appealing and realistic.
Furthermore, SVGDreamer introduces Reward Feedback Learning (ReFL) to accelerate the convergence of VPSD and improve aesthetic appeal. This technique aims to optimize the synthesis process by incorporating feedback mechanisms that reward desirable features and discourage undesired behaviors. By combining reinforcement learning and VPSD, ReFL maximizes the aesthetic quality of the generated vector graphics.
Conclusion: Superiority of SVGDreamer
Extensive experiments have been conducted to validate the effectiveness of SVGDreamer, and the results demonstrate its superiority over baseline methods in terms of editability, visual quality, and diversity. The multi-disciplinary nature of SVGDreamer, incorporating techniques from computer vision, natural language processing, particle-based score distillation, and reinforcement learning, ensures that it addresses the limitations of existing text-to-SVG generation methods comprehensively.
In this paper, we find a class of Carrollian and Galilean contractions of
(extended) BMS algebra in 3+1 and 2+1 dimensions. To this end, we investigate
possible embeddings of 3D/4D Poincar'{e} into the BMS${}_3$ and BMS${}_4$
algebras, respectively. The contraction limits in the 2+1-dimensional case are
then enforced by appropriate contractions of their Poincar'{e} subalgebra. In
3+1 dimensions, we have to apply instead the analogy between the structures of
Poincar'{e} and BMS algebra. In the case of non-vanishing cosmological
constant in 2+1 dimensions, we consider the contractions of $Lambda$-BMS${}_3$
algebras in an analogous manner.
Examining Conclusions
This paper explores the concept of Carrollian and Galilean contractions of the (extended) BMS algebra in both 3+1 and 2+1 dimensions. The study focuses on investigating the potential embeddings of 3D/4D Poincaré into the BMS₃ and BMS₄ algebras, respectively. By enforcing appropriate contractions on the Poincaré subalgebra, the contraction limits in the 2+1-dimensional case are achieved. On the other hand, in the 3+1-dimensional scenario, a comparison between the structures of Poincaré and BMS algebra is necessary. The analysis also considers the contractions of Λ-BMS₃ algebras when dealing with a non-zero cosmological constant in 2+1 dimensions.
Future Roadmap
Looking ahead, several challenges and opportunities arise in the field of Carrollian and Galilean contractions of the extended BMS algebra.
Challenges:
Mathematical Complexity: Further exploration is required to fully understand the mathematical intricacies of these contractions. Researchers will face challenges in developing rigorous mathematical models and proofs to support their findings.
Data Validation: Empirical validation of these contractions using experimental data and observations can be a challenging task. It will require collaborative efforts between theoretical physicists and experimental scientists to verify the theoretical predictions.
Generalization: The current study focuses on specific dimensions (3+1 and 2+1). Generalizing these contractions to higher dimensions poses additional challenges that need to be addressed.
Opportunities:
New Insights into Fundamental Physics: Carrollian and Galilean contractions offer a deeper understanding of the connections between different algebraic structures and their relevance to fundamental physics. This research opens up opportunities to uncover novel insights and potentially revise existing theories.
Application in Cosmology: The study of contractions of Λ-BMS₃ algebras in the presence of a non-zero cosmological constant holds promise for advancing our understanding of the universe’s evolution. It may provide valuable insights into phenomena such as cosmic inflation and dark energy.
Advanced Quantum Field Theory: The findings in this paper lay the groundwork for further exploration of advanced quantum field theories. Researchers can build upon these contractions to develop new frameworks that incorporate both classical and quantum effects.
Conclusion
The examination of Carrollian and Galilean contractions of the extended BMS algebra in different dimensions yields valuable insights into the structure of Poincaré and BMS algebras. While challenges in terms of mathematical complexity, data validation, and generalization exist, the opportunities for advancing our understanding of fundamental physics, cosmology, and quantum field theory are substantial. Further research in this area promises to provide a roadmap towards uncovering new discoveries and enhancing our knowledge of the universe.
Expert Commentary: Overcoming Assumptions in Synthetic Control Methods Using Incentivized Exploration
The use of synthetic control methods (SCMs) has become increasingly prevalent in panel data settings. These methods aim to estimate counterfactual outcomes for test units by leveraging data from donor units that have remained under control. However, a critical assumption in the literature on SCMs is that there is sufficient overlap between the outcomes of the donor units and the test unit in order for accurate counterfactual estimates to be produced.
This assumption, while common, may not always hold in practice. In scenarios where units have agency over their own interventions and different subpopulations have distinct preferences, the outcomes for test units may not lie within the convex hull or linear span of the outcomes for the donor units. This limitation can significantly impact the accuracy and reliability of SCM-based analyses.
Fortunately, a recent study addresses this issue by proposing a novel approach that incentivizes units with different preferences to take interventions they would not typically consider. This method, referred to as incentivized exploration in panel data settings, combines principles from information design and online learning to provide incentive-compatible intervention recommendations to units.
By leveraging this algorithm, researchers can obtain valid counterfactual estimates using SCMs without relying on an explicit overlap assumption on unit outcomes. The proposed approach encourages units to explore interventions beyond their default preferences, ensuring a more comprehensive understanding of the underlying causal effects. This incentivized exploration not only reduces potential biases caused by selection effects but also enhances the generalizability of SCM-based studies.
The implications of this research are substantial. It offers a new perspective on addressing the limitations of SCMs in situations where overlap assumptions do not hold. By expanding the range of interventions considered by units, researchers can gain insights into the causal effects of different policy choices or interventions across a broader spectrum of scenarios.
Moreover, this novel approach opens avenues for future research. As we continue to refine and enhance the incentivized exploration algorithm, it would be valuable to explore its applicability in diverse domains, such as healthcare, economics, and public policy. Additionally, further investigation into the potential trade-offs and constraints associated with incentivizing exploration would provide a more nuanced understanding of the approach’s effectiveness.
In conclusion, this study highlights the importance of addressing assumptions in SCMs and offers a promising solution through incentivized exploration. By incentivizing units with different preferences to explore alternative interventions, researchers can overcome limitations imposed by traditional overlap assumptions. The proposed algorithm provides a valuable tool for obtaining accurate counterfactual estimates in panel data settings and opens doors for future advancements and applications in diverse fields.
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