@@ -138,30 +138,13 @@ <h1 class="title is-1 publication-title"><span class="gradient-text">DAGDiff</sp
138138 </div>
139139 </section> -->
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141- < section class ="section " style ="background-color: rgb(252, 252, 252 ); ">
141+ < section class ="section " style ="background-color: rgb(255, 255, 255 ); ">
142142 < div class ="container is-max-desktop ">
143143 < div class ="columns has-text-centered ">
144144 < div class ="column is-full-width ">
145- <!-- <h2 class="title is-3">Model Architecture</h2> -->
146- < img src ="./static/images/teaser_icra2025.svg " width ="70% ">
147- <!-- <div class="content has-text-justified my-4">
148- <b>Overview of the proposed method</b>: <b>(a)</b> Given an object point cloud P , our network
149- encodes
150- geometric features into dense feature maps. Next,
151- randomly initialized dual-arm grasps \(H\) are used to transform a fixed query cloud into query
152- points, followed by feature sampling through bilinear interpolation. Conditioned on the noise
153- step \(t\)
154- these features are passed through \(F_{\theta}\), which predicts the SDF of the query points and
155- a feature vector.
156- This vector is then used by three output heads that predict energy \((E_\alpha)\), force-closure
157- probability \((C_{\beta}^{\text{fc}})\) and
158- collision probability \((C_{\gamma}^{\text{col}})\), jointly guiding the diffusion process. <b>(b)</b> At
159- inference, denoising proceeds from random
160- intialization \((t=250\)) to refine grasps \((t=0\)). The energy head drives the generative
161- dynamics, while the force-closure and collision heads bias the generation until stable,
162- collision-free dual-arm grasps emerge.
163-
164- </div> -->
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165148 </ div >
166149 </ div >
167150 </ div >
@@ -267,32 +250,32 @@ <h2 class="title is-3">Results (Coming soon)</h2>
267250 < div class ="column is-full-width ">
268251 < h2 class ="title is-3 "> Real Life Results</ h2 >
269252 < div class ="columns is-multiline is-centered ">
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272255 < source src ="./static/videos/real_life_bucket.webm ">
273256 </ video >
274257 < h4 class ="title is-5 "> Bucket</ h4 >
275258 </ div >
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278261 < source src ="./static/videos/real_life_tray.webm ">
279262 </ video >
280263 < h4 class ="title is-5 "> Tray</ h4 >
281264 </ div >
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284267 < source src ="./static/videos/real_life_drone.webm ">
285268 </ video >
286269 < h4 class ="title is-5 "> Drone</ h4 >
287270 </ div >
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290273 < source src ="./static/videos/real_life_frypan.webm ">
291274 </ video >
292275 < h4 class ="title is-5 "> Frypan</ h4 >
293276 </ div >
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296279 < source src ="./static/videos/real_life_saucepan.webm ">
297280 </ video >
298281 < h4 class ="title is-5 "> Saucepan</ h4 >
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