Creative Visual Generation Editing Understanding

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Creative Visual Generation Editing Understanding

Creative Visual Generation Editing Understanding

@cveu_workshop

SIGGRAPH 2025 Workshop. Previously: CVPR25 SIGGRAPH24 CVPR24 ICCV23 ECCV22 ICCV21 Join https://t.co/WoG6PDzZnL

SIGGRAPH 2025 Vancouver Aug 14 Katılım Temmuz 2021
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Creative Visual Generation Editing Understanding
Deadline Update: Following news from the CVPR workshop chairs, we have revised the In-Proceedings Track deadlines: 📅 Abstract Registration: Mar 16, 2026 📄 Submission Deadline: Mar 17, 2026 Visit our website for full details and submission info: 🔗 cveu.github.io
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Creative Visual Generation Editing Understanding
📢 𝑪𝒂𝒍𝒍 𝒇𝒐𝒓 𝑷𝒂𝒑𝒆𝒓𝒔: @CVPR 2026 Workshop on AI for Creative Visual Content Generation, Editing, and Understanding (CVEU)! We are thrilled to invite submissions to the 9th installment of the CVEU workshop at CVPR 2026 in Denver!
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#CVPR2026
#CVPR2026@CVPR·
The day has arrived! #CVPR2026 reviews are scheduled to be released sometime today. NO, we don’t know the exact release time, we’ll find out together with the community. Only our Program Chairs do. Good luck to everyone! 🤞
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Mariya I. Vasileva
Mariya I. Vasileva@mariyaivasileva·
My general principle is something along the lines of, “if it’s in textbooks or a widely-adopted concept, no need to cite original paper(s)”. But here I’m referencing the all-too-common, “let’s throw the off-the-shelf LLM hammer at this problem” without comparing to some domain-specific model because it wasn’t published in the last couple of years (yet, there wasn’t an alternative SOTA reference either).
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Mariya I. Vasileva
Mariya I. Vasileva@mariyaivasileva·
Feeling a strange mix of nostalgia and confusion after wrapping up this CVPR paper review cycle. This is probably a non-exhaustive list, but here are some raw observations of themes I saw repeat across submissions. - Extreme recency bias: references rarely leave the realm of the 2020s. - Increasingly “single-context” evaluation, rather than a “multidimensional” view of the problem. Historically, the general rule of thumb has been to look for other contexts where the same problem or phenomenon appears, and substantiate claims by evaluating across multiple tasks, datasets, distributions, or domains. - Superficial treatment of prior work: critical benchmarks, models, and related work omitted. Claims introduced in prior work repackaged in contemporary context without notable contribution: I expected to see either novel problem framing, or meaningful scope expansion. - Relatedly: hallucinated bibliographies (??!) This one is new to me, and a solid reason for a desk reject. The deluge of publications in recent years seems responsible for this trend, but shouldn’t excuse it. - Weak defensibility: design choices presented without justification via measurable improvements; critical baselines and ablations missing; architectural modifications appear arbitrary — and interestingly, can often be derived from existing methods with some linear algebra magic. This one may be evergreen, though the LLM era has really exacerbated the issue. - Chaotic paper structure: I may be biased because I was academically “raised” with guidelines drilled into my head on how to write a good paper — make contributions clear, substantiate every single one of them with experimentation, clearly differentiate from prior work, introduce strong baselines and evaluation frameworks, discuss failure modes, suggest follow-up directions for future work, etc etc Many colleagues have pointed this out already, but echoing it here; research has undergone a paradigm shift.
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Mirela Ben-Chen
Mirela Ben-Chen@mirelaben·
Goodluck everyone with the SIGGRAPH deadline today! If you're waiting for CVPR reviews, please note the following from the FAQ (s2026.siggraph.org/technical-pape…)
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Anyi Rao
Anyi Rao@raoanyi·
#SIGGRAPH2025 workshop on AI for Creative Visual Content Generation, Editing and Understanding has a full house now! @siggraph @cveu_workshop
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apolinario 🌐
apolinario 🌐@multimodalart·
in vancouver for @siggraph, let's meet?! on thursday (14) i'll be presenting at the @cveu_workshop my keynote is going to be "Generative AI as a medium of expression: beyond AI as a tool"
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Creative Visual Generation Editing Understanding
Prof. Huamin Qu is the founding dean of AIS at HKUST, leads the VisLab @VisLab_HKUST, and is an award‑winning pioneer in explainable AI and creative visualization (IEEE VGTC, AI 2000, IBM Faculty Award...) and was inducted into the IEEE Visualization Academy in 2020. 🌟
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Empowering Storytellers
Empowering Storytellers@ep_storytellers·
Ep2 is live. Guests: Chris Williamson & Jacky Zheng from Weta Workshop Truth bomb on AI in film: “I fear acceleration kills the LOVE of craft.” - Chris “Using AI? You’re in a constant BATTLE” - Jacky Will personalized AI stories ERASE water-cooler moments?
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Empowering Storytellers
Empowering Storytellers@ep_storytellers·
Ep1 live! Special guest: Tony Ngai, Asia Pacific Governor of SMPTE, shares how AI is transforming filmmaking, from workflows to the challenges of control. Advice to filmmakers: "Storytelling is your golden ticket OUT of film” What excites you most about AI in storytelling?
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